Patent Publication Number: US-8989438-B2

Title: Mobile body track identification system

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     This application is a National Stage of International Application No. PCT/JP2010/063770 filed Aug. 13, 2010, claiming priority based on Japanese Patent Application Nos. 2009-191358 filed Aug. 20, 2009, and 2009-280926 filed Dec. 10, 2009, the contents of all of which are incorporated herein by reference in their entirety. 
     TECHNICAL FIELD 
     The present invention relates to a mobile body track identification system which keeps track of the position of a mobile body while discriminating the identification of the mobile body, thus determining which mobile body matches with the recognized track. Additionally, the present invention relates to a mobile body track identification method and a mobile body track identification system. 
     BACKGROUND ART 
     In general, a mobile body such as a person or an object is tracked using the correlation between positional information of a mobile body and its identification. That is, it is possible to univocally identify and track a mobile body by way of the correlation between positional information of a mobile body and its identification. Various technologies have been developed with respect to mobile body tracking techniques (see Patent Document 1, Patent Document 2). 
     Patent Document 1 discloses a person tracking device which shoots images of persons in a surveillance area with a sensor (a viewable camera) so as to extract features of persons based on video signals output from the sensor. This person tracking device learns the correlation between identifications and features of persons, thus specifying an ID of a person based on the result of learning and newly extracted features. Additionally, the person tracking device outputs tracking information including an ID of a specified person and features of the person. When it fails to track a person halfway, the person tracking device specifies an ID of a person again based on the result of learning and the features of the person with whom tracking is resumed. That is, when an interruption occurs in tracking a person, the person tracking device specifies an ID of a person again based on the features extracted at the timing of resuming tracking. 
     Patent Document 2 discloses a mobile body tracking system equipped with a surveillance camera for shooting images in a predetermined space. This mobile body tracking system obtains positional information by identifying a mobile body based on the output of a surveillance camera, thus managing the correlation between positional information and a first identification inherent in each mobile body. Additionally, the mobile body tracking system reads a second identification inherent in each mobile body, thus managing the correlation between the positional information and the second identification. The mobile body tracking system includes a position management table for managing the correlation between positional information and a third identification. Moreover, the mobile body tracking system includes a position estimation table for managing the correlation between the second identification and the third identification. The mobile body tracking system tracks a mobile body with reference to the position management table and the position estimation table. That is, the mobile body tracking system integrates detection information produced by a plurality of sensors, thus tracking a mobile body. 
     CITATION LIST PATENT LITERATURE 
     
         
         PLT 1: Japanese Patent Application Publication No. 2008-299742 (see paragraphs 0006, 0008 through 0013) 
         PLT 2: Japanese Patent Application Publication No. 2005-31955 (see paragraphs 0008, 0009) 
       
    
     SUMMARY OF THE INVENTION 
     Problem to be Solved by the Invention 
     In general, cameras are able to track mobile bodies but they do not identify mobile bodies; hence, it is impossible to determine which mobile body is being tracked with cameras. Using individual identification devices such as RFID (Radio Frequency Identification) readers which are located at various positions in a monitored area, it is possible to follow which mobile body is passing by individual identification devices, but it is impossible to follow the track of a mobile body at a remote place distanced from individual identification devices. 
     The person tracking device disclosed in Patent Document 1 combines a camera with a function of extracting features of persons, while the mobile body tracking system disclosed in Patent Document 2 achieves tracking of mobile bodies by combining a camera with a plurality of sensors. 
     When a plurality of mobile bodies exists at the same position to approach each other (or overlap with each other), interruption may occur in tracking a specific mobile body. A person tracking device or a mobile body tracking system is able to follow the track of a mobile body when it successfully extracts feature information or when it successfully extracts identification information with a sensor. When interruption occurs frequently in tracking, it is necessary to resume tracking based on newly extracted feature information or identification information, thus lowering a tracking precision of a mobile body. That is, it resumes tracking of a mobile body based on a small amount of information newly extracted after the occurrence of interruption in tracking a mobile body, wherein it is impossible to determine which mobile body matches which track being recognized with a high precision. 
     It may be possible to detect feature information or identification information in the limited situation in which the size or position of a mobile body in a viewing angle of a camera satisfies a certain condition or in which a mobile body exists in an area that allows a sensor to detect a radio tag; this prevents frequent detection of information. For this reason, numerous tracks, which cannot be correlated to identification information of a mobile body, may occur due to frequent interruption of tracking. Additionally, a track of an object, which is not subjected to tracking, may cause noise. This noise constitutes a factor of increasing the number of tracks that cannot be correlated to identification information. 
     Solution to Problem 
     The present invention is made in consideration of the aforementioned circumstances, wherein an object thereof is to provide a mobile body track identification system that is able to determine which mobile body matches which track based on identification information. Additionally, the present invention provides a mobile body track identification method and a mobile body track identification program. 
     A mobile body track identification system of the present invention includes a track-coupling candidate generation part that generates track-coupling candidates each representing a combination of tracks of a mobile body detected in a predetermined time in the past; a hypothesis generation part that generates sets of track-coupling candidate/identification pairs, which combine identification information of the mobile body, detected in the predetermined time in the past, with track-coupling candidates, so as to generate hypotheses by use of the sets of track-coupling candidate/identification pairs satisfying a predetermined condition; and a likelihood calculation part which selects an individual hypothesis so as to calculate identification likelihoods representing likelihoods of detecting the identification information with respect to tracks specified by track-coupling candidates, included in track-coupling candidate/identification pairs ascribed to the selected hypothesis, and which integrates identification likelihoods per each track-coupling candidate/identification pair so as to calculate an identification likelihood of the selected hypothesis, thus estimating a most-probable hypothesis based on the identification likelihood per each hypothesis. 
     According to the present invention, a mobile body track identification method and a mobile body track identification program, which causes a computer to execute the mobile body track identification method, comprises the steps of: generating track-coupling candidates each representing a combination of tracks of a mobile body detected in a predetermined time in the past; generating sets of track-coupling candidate/identification pairs, which combine identification information of the mobile body, detected in the predetermined time in the past, with track-coupling candidates, so as to generate hypotheses by use of the sets of track-coupling candidate/identification pairs satisfying a predetermined condition; selecting an individual hypothesis so as to calculate identification likelihoods representing likelihoods of detecting the identification information with respect to tracks specified by track-coupling candidates, included in track-coupling candidate/identification pairs ascribed to the selected hypothesis; and integrating identification likelihoods per each track-coupling candidate/identification pair so as to calculate an identification likelihood of the selected hypothesis, thus estimating a most-probable hypothesis based on the identification likelihood per each hypothesis. 
     In the above, it is possible to refer to a probability map which defines in advance detection probabilities of the identification information of a mobile body in response to positional coordinates in a tracking area. Alternatively, it is possible to refer to track/identification correlation likelihoods representing likelihoods of correlating the identification information to tracks of a mobile body. Alternatively, it is possible to refer to attribute information or mobility information of a mobile body. It is possible to estimate a most-probable hypothesis based on a track-coupling likelihood representing a likelihood of connecting tracks included in track-coupling candidates of track-coupling candidate/identification pairs. Herein, it is possible to select track-coupling candidates corresponding to track-coupling likelihoods equal to or above a threshold, thus generating hypotheses. Moreover, it is possible to use environmental information representing a position of obstruction in a tracking area and a transient time in which a mobile body passes by the obstruction, or it is possible to use environmental information representing a transient time in which a mobile body passes through a passable path in the tracking area. In this case, it is necessary to calculate a real transient time from a track disappearance detection time to a next track appearance detection time with respect to each track-coupling candidate, and it is necessary to estimate a transient time of a mobile body based on environmental information. Track-coupling candidates, in which the estimated transient time is longer than the real transient time, are precluded from track-coupling candidates for use in generation of hypotheses. 
     Advantageous Effects of the Invention 
     As described above, a most-probable hypothesis is estimated based on a combination of a plurality of parameters, so that it is possible to estimate a disappearing part of a track with a high precision even when an interruption occurs in tracking a mobile body. That is, even when an interruption frequently occurs in following a track of a mobile body, it is possible to connect tracks of a mobile body with a high precision, thus significantly improving a tracking performance. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  A block diagram showing a mobile body track identification system according Embodiment 1 of the present invention. 
         FIG. 2  A block diagram showing the constitution of a mobile body track identification device in the mobile body track identification system. 
         FIG. 3  A block diagram showing the constitution of a likelihood calculation part of the mobile body track identification device. 
         FIG. 4  A flowchart showing the operation of the mobile body track identification device. 
         FIG. 5  A flowchart showing the operation of the mobile body track identification device. 
         FIG. 6  An illustration of examples of detected tracks of mobile bodies at prescribed times in a tracking area. 
         FIG. 7  An illustration of the tracking area which is grid-divided into a plurality of cells, thus setting identification probability maps with two identification information detection devices  2   a ,  2   b.    
         FIG. 8  An illustration of an identification probability map of the identification information detection device  2   a.    
         FIG. 9  An illustration of an identification probability map of the identification information detection device  2   b.    
         FIG. 10  A table showing the correlation between the numbers of identification information detection devices, identifications, and detection times. 
         FIG. 11  An illustration of an estimation procedure of tracks  1 ,  3  based on probabilities produced by the identification information detection device  2   a  in the tracking area. 
         FIG. 12  An illustration of an estimation procedure of tracks  1 ,  3  based on probabilities produced by the identification information detection device  2   b  in the tracking area. 
         FIG. 13  An illustration of an estimation procedure of tracks  2 ,  4  based on probabilities produced by the identification information detection device  2   a  in the tracking area. 
         FIG. 14  An illustration of an estimation procedure of tracks  2 ,  4  based on probabilities produced by the identification information detection device  2   b  in the tracking area. 
         FIG. 15  A block diagram showing the constitution of a mobile body track identification device in a mobile body track identification system according to Embodiment 2 of the present invention. 
         FIG. 16  A block diagram showing the constitution of a likelihood calculation part of the mobile body track identification device. 
         FIG. 17  A flowchart showing the operation of the mobile body track identification device according to Embodiment 2. 
         FIG. 18  A flowchart showing the operation of the mobile body track identification device according to Embodiment 2. 
         FIG. 19  A block diagram showing the constitution of a mobile body track identification device in a mobile body track identification system according to Embodiment 3 of the present invention. 
         FIG. 20  A block diagram showing the constitution of a likelihood calculation part of the mobile body track identification device. 
         FIG. 21  A flowchart showing the operation of the mobile body track identification device according to Embodiment 3. 
         FIG. 22  A flowchart showing the operation of the mobile body track identification device according to Embodiment 3. 
         FIG. 23  An illustration explaining an effect of likelihood integration in Embodiment 3. 
         FIG. 24  A block diagram showing the constitution of a mobile body track identification device in a mobile body track identification system according to Embodiment 4 of the present invention. 
         FIG. 25  A block diagram showing the constitution of a likelihood calculation part of the mobile body track identification device. 
         FIG. 26  A flowchart showing the operation of the mobile body track identification device according to Embodiment 4. 
         FIG. 27  A flowchart showing the operation of the mobile body track identification device according to Embodiment 4. 
         FIG. 28  A block diagram showing the constitution of a mobile body track identification device in a mobile body track identification system according to Embodiment 5 of the present invention. 
         FIG. 29  A block diagram showing the constitution of a likelihood calculation part of the mobile body track identification device. 
         FIG. 30  A flowchart showing the operation of the mobile body track identification device according to Embodiment 5. 
         FIG. 31  A flowchart showing the operation of the mobile body track identification device according to Embodiment 5. 
         FIG. 32  A block diagram showing the constitution of a mobile body track identification device in a mobile body track identification system according to Embodiment 6 of the present invention. 
         FIG. 33  A block diagram showing the constitution of a likelihood calculation part of the mobile body track identification device. 
         FIG. 34  A flowchart showing the operation of the mobile body track identification device according to Embodiment 6. 
         FIG. 35  A flowchart showing the operation of the mobile body track identification device according to Embodiment 6. 
         FIG. 36  A block diagram showing the constitution of a mobile body track identification device in a mobile body track identification system according to Embodiment 7 of the present invention. 
         FIG. 37  A diagrammatic illustration of a rejection decision procedure on track-coupling candidates according to environmental information of an obstruction map in Embodiment 7. 
         FIG. 38  An illustration indicative of a comparison between a real transit time, which is calculated between a track-disappearing time and a next track-appearing time, and an estimated transit time, which is calculated based on track-disappearing position coordinates and next track-appearing position coordinates, in each track-coupling candidate with an interruption of its track. 
         FIG. 39  A diagrammatic illustration of a rejection decision procedure on track-coupling candidates according to environmental information of a path graph in Embodiment 7. 
         FIG. 40  A flowchart showing the operation of the mobile body track identification device according to Embodiment 7. 
         FIG. 41  A block diagram showing the minimum constitution of a mobile body track identification system according to the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     The embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, corresponding parts are designated by the same reference numerals; hence, duplicate descriptions thereof will be omitted. 
     Embodiment 1 
       FIG. 1  is a block diagram of a mobile body track identification system according to Embodiment 1 of the present invention. This mobile body track identification system includes a positional information detection device  1 , an identification information detection device  2 , a mobile body track identification device  3 , and an identification result output device  4 . The mobile body track identification system identifies which mobile body matches which track in a tracking area  50 . That is, it is possible to recognize the position where a mobile body P exists. 
     The mobile body P freely moves in the tracking area  50 . Additionally, the mobile body P can move outside the tracking area  50 . In this connection, the type of a mobile body is not limited to a particular one; hence, it can be a person, an animal, or an object. 
     The positional information detection device  1  detects the position of a mobile body in the tracking area  50 . In the following description, each position in the tracking area  50  is detected as two-dimensional coordinates. The positional information detection device  1  sends a combination of two-dimensional coordinates (hereinafter, simply referred to as “positional coordinates”) of a mobile body in the tracking area  50 , its detection time, and its track ID to the mobile body track identification device  3 . The detected positional coordinates may be referred to as positional information. 
     The track ID constituting an identification is allocated to discriminate the positional information of the same mobile body with respect to positional information of a mobile body successfully involved in consecutive detection. That is, a series of positional coordinates ascribed to the same track ID collectively indicates a track of the same mobile body. When positional detection of a mobile body fails at a certain time, however, an interruption occurs in detecting a track of a mobile body. For this reason, when positional detection is successfully performed on a mobile body again after positional information of a mobile body fails, a new track ID is allocated to the mobile body. Embodiment 1 indicates track IDs in the numbering form (i.e. track numbers), but it is possible to indicate track IDs in another form than the numbering form. 
     The positional information detection device  1  is required to detect positional coordinates of mobile bodies in the tracking area  50 , to specify detection times, and to allocate track numbers. For instance, the positional information detection device  1  may be realized using a floor pressure sensor, a GPS (Global Positioning System), or the like. 
     It is preferable that the positional information detection device  1  be arranged to detect the entirety of the tracking area  50  without blind spots, although it may be permissible to include blind spots causing partial undetectability. This is because the mobile body track identification device  3  is able to connect fragments of a track and to identify which mobile body matches with the track. 
     The identification information detection device  2  obtains an identification of a mobile body in the tracking area  50 . Even when a mobile body exists in the tracking area  50 , the identification information detection device  2  may not always detect its identification; hence, a probability of actually detecting an identification of a mobile body depends on two-dimensional positional coordinates of a mobile body in the tracking area  50 . For instance, a mobile body, which is positioned proximate to the identification information detection device  2 , provides a high probability of detecting its identification, whilst a mobile body, which is distanced from the identification information detection device  2 , provides a low probability of detecting its identification. 
     The mobile body track identification system of  FIG. 1  adopts a single identification information detection device  2 , although it is possible to arrange a plurality of identification information detection devices  2  to cover the entirety of the tracking area  50 . An identification detection device ID is allocated to the identification information detection device  2  to univocally specify it. The identification detection device ID is used to discriminate which identification information detection device  2  detects identification. Embodiment 1 indicates identification detection device IDs in the numbering form (i.e. identification detection device numbers), although it is possible to indicate them in another form than the numbering form. 
     The identification information detection device  2  sends a combination of detected identification, its detection time, and its identification detection device number to the mobile body track identification device  3 . When the identification information detection device  2  tries to detect an identification of a mobile body but fails to detect it, a combination of identification “Null”, its time, and its identification detection device number is sent to the mobile body track identification device  3 . Alternatively, the mobile body track identification device  3  may determine that no identification is detected at a prescribed time when the identification information detection device  2  does not send identification to the mobile body track identification device  3 . 
     The identification information detection device  2  is required to detect identification and to specify its detection time and its identification detection device number. For instance, it is possible to employ an RFID reader as the identification information detection device  2  since an active RFID tag is attached to a mobile body and used as its identification. When a mobile body represents a person, it is possible to employ a reader device for reading identification, such as a person&#39;s face, fingerprints, and veins, as the identification information detection device  2 . Additionally, it is possible to combine devices for detecting identifications of different samples, such as a face recognition device and a RFID reader. 
     In order to cover the tracking area  50  by use of a plurality of identification information detection devices  2 , it is possible to arrange the identification information detection devices  2  such that their detection ranges overlap with each other. Alternatively, it is possible to arrange them such that their detection ranges do not overlap with each other. 
     The positional information detection device  1  for detecting positional coordinates needs to operate in synchronism with the identification information detection device  2  for detecting identifications. That is, the positional information detection device  1  detects positional coordinates of a mobile body while the identification information detection device  2  detects its identification at the same time. When the positional information detection device  1  for detecting positional coordinates operates in asynchronism with the identification diction device  3  for detecting identifications, the mobile body track identification device  3  retains positional coordinates and identifications in a buffer for a certain time; then, positional coordinates and identifications accumulated in the buffer can be used every predetermined time. Alternatively, when no timing synchronization is established between the positional information detection device  1  and the identification information detection device  2 , it is possible to set the same detection time with respect to positional coordinates and identification simultaneously received by the mobile body track identification device  3 . 
     By use of positional information of the positional information detection device  1  and identification of the identification information detection device  2 , the mobile body track identification device  3  determines which mobile body matches which track. When the positional information detection device  1  is interrupted in tracking, it detects fragments of a track of a mobile body. In this case, the mobile body track identification device  3  sets combinations of tracks of mobile bodies specified by track numbers as track-coupling candidates, determining a most-probable combination of identification and its track-coupling candidate. In this sense, each track-coupling candidate may define one connecting fragment of a track of a mobile body. 
       FIG. 2  is a block diagram showing the constitution of the mobile body track identification device  3 . The mobile body track identification device  3  is connected to the positional information detection device  1 , the identification information detection device  2 , and the identification result output device  4 . The mobile body track identification device  3  includes a track-coupling candidate generation part  31  that implements connection processing on fragments of tracks of mobile bodies, a hypothesis generation part  32  that generates hypotheses representing a set of pairs of track-coupling candidates and identifications, a likelihood calculation part  33  that calculates likelihoods of hypotheses so as to specify a most-probable hypothesis, and a map storage  37 . Specifying one hypothesis makes it possible to determine a set of pairs of track-coupling candidates and identifications. This indicates that a correlation is established between a detected track of a mobile body and the mobile body P in the tracking area  50 . 
     The track-coupling candidate generation part  31  receives a combination of positional information (i.e. positional coordinates), its detection time, and its track number from the positional information detection device  1 . A set of positional coordinates, which are ascribed to the same track number and which are detected in a time range from a predetermined time in the past to the current time, indicates a single track of a mobile body. The track-coupling candidate generation part  31  generates combinations of tracks of individual mobile tracks so as to send them as track-coupling candidates to the hypothesis generation part  32 . 
     When the number of tracks is N, the track-coupling candidate generation part  31  generates all types of track-coupling candidates ranging from a track-coupling candidate including one track and a track-coupling candidate including N tracks. When three tracks are detected, for example, the track-coupling candidate generation part  31  generates “track  1 ”, “track  2 ”, and “track  3 ” as a track-coupling candidate including one track. As a track-coupling candidate including two tracks, it generates “track  1 , track  2 ”, “track  1 , track  3 ”, and “track  2 , track  3 ”. As a track-coupling candidate including three tracks, it generates “track  1 , track  2 , track  3 ”. Thus, the track-coupling candidate generation part  31  generates seven track-coupling candidates in connection with three tracks. In this connection, the track-coupling candidate generation part  31  does not generate track-coupling candidates combining the same tracks. For this reason, no duplication occurs among track-coupling candidates. 
     A plurality of tracks with positional coordinates detected at the same time may be included in track-coupling candidates. In a track-coupling candidate of “track  1 , track  2 ” in which positional coordinates of track  1  and positional coordinates of track  2  are detected at the same time, for example, the track-coupling candidate generation part  31  calculates median points among positional coordinates detected at the same time. When coordinates A of track  1  and coordinates B of track  2  are detected at the same time, for example, the track-coupling candidate generation part  31  calculates a median point between coordinates A and B (i.e. a midpoint between two coordinates). The same calculation is performed on other combinations of coordinates detected at the same time. An identification likelihood, described below, is calculated with reference to coordinates, wherein when a plurality of coordinates is detected at the same time, the track-coupling candidate generation part  31  calculates a median point among a plurality of coordinates so as to refer to it. 
     The hypothesis generation part  32  receives identifications, their detection times, and their identification detection device numbers from the identification information detection device  2 , thus holding their correlations. Additionally, the hypothesis generation part  32  generates all the combinations of track-coupling candidates and their identifications, thus providing them to the likelihood calculation part  33  as hypotheses. 
     Next, a hypothesis generation procedure will be described in detail. First, the hypothesis generation part  32  generates all combinations of track-coupling candidates and identifications based on track-coupling candidates of the track-coupling candidate generation part  31  and identifications which are detected by the identification information detection device  2  in a time range from a predetermined time in the past to the current time. 
     Considering a possibility leading to excessiveness or omissiveness in detecting tracks and identifications by the positional information detection device  1  and the identification information detection device  2 , it is possible to presume the situation in which tracks and identifications, which are not actually correlated to their counterparts, may be detected. Herein, the excessiveness in detecting tracks indicates an event in which tracks of objects other than a tracked object are mistakenly detected. The excessiveness in detecting identifications indicates an event in which even when a mobile body having an RFID tag exists outside the tracking area, its identification is detected by an RFID reader located in the tracking area due to radio interference or an event in which when a person recognized as a mobile body passes in front of the identification information detection device  2  adopting a biometric identification technology (or a face recognition technology), its identification is mistakenly detected. Additionally, the omissiveness in detecting tracks indicates the impossibility of detecting a track of a mobile body which moves in blind spots of the positional information detection device  1 . The omissiveness in detecting identifications indicates an event in which even when a mobile body having an RFID tag exists in the detection area of an RFID reader, its identification is not detected since radio reception is weakened due to radio interference or an event in which when a person recognized as a mobile body passes in front of the identification information detection device  2  adopting a biometric identification technology (or a face recognition technology), its picture with an ideal face direction and an ideal size is not produced so that its identification cannot be detected. To cope with these drawbacks, the hypothesis generation part  32  additionally generates information, representing nonexistence of counterparts (i.e. counterpart track-coupling candidates and counterpart identifications), which is paired with all pairs of track-coupling candidates and identifications. In this connection, the information representing the nonexistence of counterparts is specified using a character string of “unknown”. When the track-coupling candidate generation part  31  generates track-coupling candidates T 1 , T 2 , . . . , Tn whilst the identification information detection device  2  detects identifications ID 1 , ID 2 , . . . , IDm, for example, the hypothesis generation part  32  generates pairs of (T 1 , unknown), (T 2 , unknown), . . . , (Tn, unknown) and pairs of (unknown, ID 1 ), (unknown, ID 2 ), . . . , (unknown, IDm), which are added to the foregoing pairs of track-coupling candidates and identifications. 
     In this connection, pairs of track-coupling candidates and identifications will be referred to as “track-coupling candidate/identification pairs”. Pairs including “unknown” are equivalent to track-coupling candidate/identification pairs. 
     The hypothesis generation part  32  selects a set of track-coupling candidate/identification pairs satisfying prescribed conditions as a hypothesis. There is a possibility that a plurality of sets of track-coupling candidate/identification pairs satisfying prescribed conditions may exist. For this reason, the hypothesis generation part  32  extracts all the sets of track-coupling candidate/identification pairs satisfying prescribed conditions, thus determining these sets as individual hypotheses. 
     It is possible to name three conditions as prescribed conditions for determining hypotheses corresponding to sets of track-coupling candidate/identification pairs, as follows. 
     A first condition refers to non-repetition of tracks and identifications among track-coupling candidate/identification pairs ascribed to each hypothesis. When identifications ID 1 , ID 2  are detected in connection with four tracks k 1 , k 2 , k 3 , and k 4 , for example, a set of [{(k 1 , k 2 ), ID 1 }, {(k 1 , k 3 , k 4 ), ID 2 }] does not satisfy the first condition because of repetition of the track k 1 . A set of [{(k 1 , k 2 ), ID 1 }, {(k 3 , k 4 ), ID 1 }] does not satisfy the first condition because of repletion of the identification ID 1 . On the other hand, a set of [{(k 1 , k 2 ), ID 1 }, {(k 3 , k 4 ), ID 2 }] satisfies the first condition because of non-repetition of tracks and identifications. Herein, (k 1 , k 2 ) indicates a track-coupling candidate, while {(k 1 , k 2 ), ID 1 } indicates a track-coupling candidate/identification pair. Each hypothesis is made on precondition that a single track of a mobile body is included in a single track-coupling candidate. Hypotheses are candidates used for determining which mobile body is connected to which track, wherein the first condition is needed because hypotheses including repetition of tracks or identifications cannot be determined as appropriate candidates. 
     However, the present embodiment allows for repetition of “unknown” serving as identifications. That is, the present embodiment shall not determine a failure to meet the first condition even when repletion of “unknown” occurs in identifications. 
     A second condition refers to inclusion of individual tracks, specified by track numbers, into track-coupling candidates ascribed to all the track-coupling candidate/identification pairs. In the foregoing case in which the tracks k 1  to k 4  and the identifications ID 1 , ID 2  are detected, a set of [{(k 1 , k 2 ), ID 1 }] includes only the tracks k 1 , k 2  in a track-coupling candidate/identification pair but does not include the tracks k 3 , k 4 . Therefore, this set does not satisfy the second condition. On the other hand, a set of [{(k 1 , k 2 ), ID 1 }, {(k 3 , k 4 ), ID 2 }] includes all the tracks in track-coupling candidate/identification pairs, thus satisfying the second condition. 
     A third condition refers to inclusion of at least one track-coupling candidate/identification pair whose identification is not “unknown” in a set of track-coupling candidate/identification pairs. In the foregoing case in which the tracks k 1  to k 4  and the identifications ID 1 , ID 2  are detected, identifications “unknown” are found in two track-coupling candidate/identification pairs included in a set of [{(k 1 , k 2 ), unknown}, {(k 3 , k 4 ), unknown}, which thus does not satisfy the third condition. On the other hand, a set of [{(k 1 , k 2 ) ID 1 }, {(k 3 , k 4 ), unknown}] includes a track-coupling candidate/identification pair whose identification is not “unknown”, thus satisfying the third condition. 
     The hypothesis generation part  32  selects all the sets of track-coupling candidate/identification pairs satisfying the first to third conditions as hypotheses. When the identifications ID 1 , ID 2  are detected, for example, it is possible to establish a hypothesis using a set with one track-coupling candidate/identification pair including ID 1  and another track-coupling candidate/identification pair including an identification of “unknown”. Similarly, it is possible to establish a hypothesis using a set with one track-coupling candidate/identification pair including ID 2  and another track-coupling candidate/identification pair including an identification of “unknown”. Additionally, it is possible to establish a hypothesis using a set with one track-coupling candidate/identification pair including ID 1 , another track-coupling candidate/identification pair including ID 2 , and a further track-coupling candidate/identification pair including an identification of “unknown”. It is possible to establish a hypothesis using a set consisting of track-coupling candidate/identification pairs without identifications of “unknown”. The hypothesis generation part  32  selects hypotheses covering all the sets satisfying the first to third conditions. 
     The map storage  37  stores an identification likelihood map. The identification likelihood map will be described later. 
     In Embodiment 1, the likelihood calculation part  33  calculates two types of likelihoods per each hypothesis. A first likelihood is a likelihood of connecting tracks included in track-coupling candidates in track-coupling candidate/identification pairs ascribed to each hypothesis. The first likelihood will be referred to as a track-coupling likelihood. A second likelihood is a likelihood of detecting identifications with respect to tracks specified by track-coupling candidates in track-coupling candidate/identification pairs. The second likelihood will be referred to as an identification likelihood or an identification detection likelihood. An identification likelihood is represented by a conditional probability P(OiS) using a track S and an identification O specified by a track-coupling candidate, 
     In this connection, an identification likelihood is calculated per each track-coupling candidate/identification pair, so that the result of integrating identification likelihoods for track-coupling candidate/identification pairs ascribed to each hypothesis is used as an identification likelihood per each hypothesis. 
       FIG. 3  is a block diagram showing the constitution of the likelihood calculation part  33  in Embodiment 1. The likelihood calculation part  33  includes a likelihood calculation control part  330 , a track-coupling likelihood calculation part  331 , and an identification likelihood calculation part  332 . The likelihood calculation control part  330  sends hypotheses with uncalculated likelihoods to the track-coupling likelihood calculation part  331  and the identification likelihood calculation part  332 . The track-coupling likelihood calculation part  331  calculates a track-coupling likelihood of a track-coupling candidate per each track-coupling candidate/identification pair ascribed to each hypothesis. The identification likelihood calculation part  332  calculates identification likelihoods for track-coupling candidate/identification pairs per each hypothesis. Additionally, the identification likelihood calculation part  332  determines the result of integrating calculations of two types of likelihoods (i.e. a track-coupling likelihood and an identification likelihood) as a likelihood per each hypothesis, so that the most-probable estimation result indicating a hypothesis having the highest likelihood is sent to the identification result output device  4 . 
     Next, the operation of the track-coupling likelihood calculation part  331  calculating track-coupling candidates will be described. To simplify explanation, the following description refers to the operation of calculating track-coupling candidates combining two tracks. 
     Combinations of track-coupling candidates may include a combination of tracks which are repetitively detected at the same time and another combination of tracks which are independently detected at different times. 
     First, the operation of calculating a track-coupling likelihood of a track-coupling candidate combining tracks which are repetitively detected at the same time will be described. The track-coupling likelihood calculation part  331  calculates euclidean distances between coordinates which are detected at the same time per each track, thus determining track-coupling likelihoods such that higher track-coupling likelihoods are determined with respect to shorter euclidean distances while lower track-coupling likelihoods are determined with respect to longer euclidean distances. An event in which tracks are detected at the same time indicates a high possibility of existence of a plurality of pairs of coordinates which are detected at the same time. Specifically, the track-coupling likelihood calculation part  331  calculates a euclidean distance, between coordinates detected at the same time, per each pair of coordinates, thus calculating an average value. Track-coupling likelihoods are determined such that a higher track-coupling likelihood is determined for a smaller average value of euclidean distances, each calculated per each pair of coordinates, whilst a lower track-coupling likelihood is determined for a larger average value. Herein, a track-coupling likelihood is determined using an average value of euclidean distances between coordinates, but it is possible to use a variance value instead of an average value. When two tracks do not coincide with each other in terms of the start time and the end time of detecting coordinates, the aforementioned process is carried out in a time range in which coordinates of two tracks are detected simultaneously. 
     The track-coupling likelihood calculation part  331  may calculate a mobile vector per each track, so that a cosine similarity of a mobile vector is calculated as a track-coupling likelihood. A mobile vector of each track is a vector with its start point, specified by coordinates used for starting simultaneous detection of coordinates of two tracks, and its end point, specified by stopping simultaneous detection of coordinates. Herein, the track-coupling likelihood calculation part  331  calculates mobile vectors V 1 , V 2  according to Equation 1 so as to calculate a cosine similarity cos θ, thus determining a track-coupling likelihood. 
     
       
         
           
             
               
                 
                   
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                     ⁢ 
                     
                         
                     
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                     ⁢ 
                     
                         
                     
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     In Equation 1,  V 1 ,V 2    indicates a scalar product of mobile vectors V 1 , V 2 , while ∥V 1 ∥ indicates a norm of the mobile vector V 1 , and ∥V 2 ∥ indicates a norm of the mobile vector V 2 . 
     In this connection, the likelihood calculation part  33  may determine a threshold for track-coupling likelihoods in advance, so that track-coupling likelihoods less than the threshold are precluded from the processing thereafter. 
     Next, the operation of calculating track-coupling likelihoods of track-coupling candidates combining tracks which are independently detected at different times will be described. Herein, the track-coupling likelihood calculation part  331  calculates a mobility of a mobile body based on the disappearance time and positional coordinates of a preceding track and the appearance time and positional coordinates of a subsequent track. The track-coupling likelihood calculation part  331  calculates track-coupling likelihoods such that a higher track-coupling likelihood is calculated for a smaller error between an actually calculated mobility and a mobility of a mobile body, which is selected as a mobile model in advance, whilst a lower track-coupling likelihood is calculated for a larger error. 
     Considering the halfway station of a mobile body, it is possible to determine a likelihood function which hardly decreases a track-coupling likelihood when a negative error is produced by subtracting a mobility of a preselected mobile model from a calculated mobility while steadily decreasing a track-coupling likelihood when a positive error is produced. Specifically, mobility data are collected upon occurrence of interruption in tracking, so that sets of mobility with positive errors between the mobility of a mobile model and the collected mobility data are separated from sets of mobility with negative errors, wherein an average value and a variance value are calculated per each set. Replicate sets are produced with respect to sets with positive errors, wherein replicate sets are reversed with negative signs and then added to original sets, thus producing a zero average. On the other hand, replicate sets are produced with respect to sets with negative errors, wherein replicate sets are reversed with positive signs and then added to original sets, thus producing a zero average. The distribution of errors included in these sets is presumed as normal distribution, wherein sets with positive errors have an average μs and a variance σs. Similarly, sets with negative errors have an average μm and a variance σm. In this case, a probability density function is defined as Equation 2. 
     
       
         
           
             
               
                 
                   
                     
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     The track-coupling likelihood calculation part  331  uses a calculated error (i.e. a value produced by subtracting a mobility of a preselected mobile model from a calculated mobility) as a variable x, which is put into Equation 2, thus calculating a track-coupling likelihood. 
     Additionally, a threshold for track-coupling candidates combining tracks which are independently detected at different times is determined in advance, so that track-coupling likelihoods less than the threshold are precluded from the processing thereafter. 
     The above procedure is related to track-coupling candidates each combining two tracks. With respect to track-coupling candidates each combining three tracks, the track-coupling likelihood calculation part  331  calculates track-coupling likelihoods according to the following procedure. 
     First, the track-coupling likelihood calculation part  331  sequences tracks included in track-coupling candidates in an order of earlier disappearance times. 
     Next, the track-coupling likelihood calculation part  331  calculates a track-coupling likelihood with respect to two tracks which adjoin in their places of ordering. That is, the track-coupling likelihood calculation part  331  calculates a track-coupling likelihood with respect to tracks of first and second places of ordering regarding their disappearance times. Similarly, it calculates a track-coupling likelihood with respect to tracks of second and third places of ordering regarding their disappearance times; subsequently, it calculates a track-coupling likelihood with respect to tracks of third and fourth places of ordering regarding their disappearance times. In this connection, the foregoing method is used to calculate a track-coupling likelihood with respect to two tracks which adjoin in their places of ordering. 
     After calculating track-coupling likelihoods with respect to pairs of tracks which adjoin in their places of ordering, the track-coupling likelihood calculation part  331  multiplies the calculation results of track-coupling likelihoods so as to calculate a radical root of the number of multiplication times. This calculation result is used as a track-coupling likelihood regarding a combination of three or more tracks. In this connection, the calculation of a radical root implements normalization of track-coupling likelihoods. 
     The track-coupling likelihood calculation part  331  sets a constant (e.g. “1”) to a track-coupling likelihood of a track-coupling candidate consisting of a single track. 
     The identification likelihood calculation part  332  calculates an identification likelihood per each hypothesis generated by the hypothesis generation part  32 . The identification likelihood calculation part  332  calculates identification likelihoods with reference to an identification probability map which is preselected per each identification information detection device  2 . The identification probability map is a map, in which the tracking area  50  is subjected to grid-dividing to produce cells and which defines values, ranging from 0 to 1, representing a probability of the identification information detection device  2  detecting an identification in each cell. 
     As described above, the present embodiment does not employ two values representing the inside and the outside of a certain area to define a detection area for an identification, but the present embodiment defines a probability distribution for detecting identifications in the entire tracking area  50 , whereby it is possible to obtain a detection probability of an identification at a specific position irrespective of the location of a mobile body in the tracking area  50 . The identification likelihood calculation part  332  calculates identification likelihoods for track-coupling candidate/identification pairs. The identification likelihood calculation part  332  calculates an identification likelihood based on a detection probability of an identification, which has been detected in a time range from a predetermined time in the past to the current time, with reference to the identification probability map; hence, it is possible to discriminate the correlation between a track and an identification without depending on identification detected at the current time. Irrespective of a low detection probability of an identification at the current time, it is possible to discriminate the correlation between a track and an identification with a high precision on the condition that a mobile body passed a position of a high detection probability in the past. 
     The identification likelihood calculation part  332  calculates an identification likelihood per each hypothesis in accordance with the following procedure. 
     First, the identification likelihood calculation part  332  extracts track-coupling candidate/identification pairs included in a hypothesis. The identification likelihood calculation part  332  calculates and multiplies identification likelihoods of track-coupling candidate/identification pairs. Since the number of track-coupling candidate/identification pairs included in each hypothesis is not fixed, there exist track-coupling candidate/identification pairs with uncalculated identifications. For this reason, the identification likelihood calculation part  332  normalizes the multiplication result of identification likelihoods calculated for track-coupling candidate/identification pairs respectively. 
     In order to individually calculate identification likelihoods with respect to track-coupling candidate/identification pairs, the identification likelihood calculation part  332  refers to a detection time for detecting an identification of a track-coupling candidate/identification pair which is currently focused on, and the identification detection device number of the identification information detection device  2  detecting the identification. Additionally, the identification likelihood calculation part  332  selects an identification probability map specified by the identification detection device number. The identification probability map per each identification detection device number is stored in the map storage  37  in advance. In this connection, the identification likelihood calculation part  332  retrieves the correlation between the identification, its detection time, and the identification detection device number of the identification information detection device  2  detecting the identification from the hypothesis generation part  32 . 
     The track-coupling candidate generation part  31  provides the identification likelihood calculation part  332  with the correlation between positional coordinates of a mobile body, its detection time, and its track number. The track-coupling candidate generation part  31  additionally provides coordinates of a median point when positional coordinates of different tracks, included in a track-coupling candidate, are detected at the same time. The identification likelihood calculation part  332  determines which cell in an identification probability map corresponds to positional coordinates with reference to coordinates of a track-coupling candidate at the detection time of its identification, thus retrieving an identification likelihood defined in the cell. When positional coordinates of tracks, included in a track-coupling candidate, are detected at the same time so that a median point thereof is calculated, the identification likelihood calculation part  332  specifies a cell corresponding to coordinates of the median point, thus retrieving an identification likelihood defined in the cell. 
     The identification likelihood calculation part  332  multiplies identification likelihoods, which are obtained at all the detection times of identifications. The multiplication result is used as an identification likelihood for a track-coupling candidate/identification pair which is currently focused on. 
     In his connection, the identification likelihood calculation part  332  does not calculate identification likelihoods with respect to track-coupling candidate/identification pairs with uncalculated identification likelihoods. That is, an identification likelihood is not calculated with respect to a track-coupling candidate/identification pair with an identification or a track-coupling candidate indicating “unknown”. 
     The identification likelihood calculation part  332  calculates and multiplies identification likelihoods per each track-coupling candidate/identification pair ascribed to a hypothesis, wherein it subsequently normalizes the multiplication result to produce an identification likelihood representing the entirety of the hypothesis. Since an identification likelihood per each track-coupling candidate/identification pair is equivalent to the product of probabilities defined in an identification probability map, the multiplication result of identification likelihoods per each track-coupling candidate/identification pair is equivalent to the produce of probabilities. Therefore, it is necessary to specify the number of probabilities, defined in an identification likelihood map, which are involved in the product, whereby it is possible to normalize the multiplication result of identification likelihoods per each track-coupling candidate/identification pair by calculating a radical root of the number of probabilities. This normalization makes it possible to appropriately compare identification likelihoods among hypotheses irrespective of the different numbers of track-coupling candidate/identification pairs ascribed to hypotheses. 
     The identification likelihood calculation part  332  repeats the above procedure to calculate identification likelihoods with respect to all the hypotheses. 
     Additionally, the identification likelihood calculation part  332  integrates a track-coupling likelihood and an identification likelihood per each hypothesis, thus producing an overall likelihood per each hypothesis. It can be said that a hypothesis having the highest likelihood may appropriately represent the relationship between tracks and identifications. The identification likelihood calculation part  332  selects a hypothesis having the highest likelihood so as to send it to the identification result output device  4 . 
     The identification result output device  4  presents the correlation between a track and a mobile body based on the hypothesis (i.e. a most-probable estimation result) sent from the identification likelihood calculation part  332 . Since individual track-coupling candidate/identification pairs ascribed to a hypothesis represent the correlations between tracks and identifications, it is possible to determine and present which mobile body is correlated to which track based on track-coupling candidate/identification pairs ascribed to a hypothesis. At this time, coordinates at the current time or tracks detected in a predetermined time in the past are presented as well. For instance, the identification result output device  4  is configured of a display. That is, the identification result output device  4  includes a display so as to show the correlation between a track and a mobile body on the display. In this connection, the output mode of the identification result output device  4  is not necessarily limited to a display mode; hence, it is possible to employ other output modes such as a print output. 
     The track-coupling candidate generation part  31 , the hypothesis generation part  32 , and the likelihood calculation part  33  (i.e. the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , and the identification likelihood calculation part  332 ) is realized using a CPU of a computer which operates in accordance with a mobile body track identification program. That is, a program memory of a computer (not shown) stores a mobile body track identification program; a CPU loads the mobile body track identification program, thus realizing the functions of the track-coupling candidate generation part  31 , the hypothesis generation part  32 , and the likelihood calculation part  33  according to the program. In this connection, it is possible to realize the track-coupling candidate generation part  31 , the hypothesis generation part  32 , and the likelihood calculation part  33  by use of individual hardware elements. Additionally, it is possible to realize the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , and the identification likelihood calculation part  332  by use of individual hardware elements. 
     Next, the operation of the mobile body track identification device  3  will be described in detail with reference to the flowcharts of  FIGS. 4 and 5  and concrete examples of  FIGS. 6 to 14 . 
       FIG. 6  shows positions and track numbers at times t 1  to t 9  in connection with a mobile body A and a mobile body B which moves in the tracking area  50 . Herein, the mobile body A corresponds to tracks  1 ,  3  while the mobile body B corresponds to tracks  2 ,  4 . Additionally, the identification of the mobile body A is solely detectable whilst the identification of the mobile body B has not been registered yet so that its identification does not exist. 
       FIG. 7  shows the positions of an identification information detection device  2   a  and an identification information detection device  2   b , arranged in the tracking area  50 , as well as grid divisions which are used to define an identification probability map, wherein the lower-left corner indicates an origin (0,0) so that p(x,y) may represent arbitrary positional coordinates. Additionally, c(a,b) represents coordinates of an arbitrary cell on the identification probability map. It is possible to arbitrarily set the number of divisions corresponding to cells, whereas  FIG. 7  shows the setting with a range of 0 to 8 in a direction a and a range of 0 to 4 in a direction b. That is, the identification information detection devices  2   a ,  2   b  are positioned at cell coordinates c(0,4) and c(8,0). 
     A probability ranging from 0 to 1 according to the property of each identification information detection device is defined in each cell on the identification probability map of  FIG. 7 . Herein, detection probabilities of identifications are defined in cells with respect to the two identification information detection devices  2   a ,  2   b .  FIG. 8  shows an identification probability map of the identification information detection device  2   a , whilst  FIG. 9  shows an identification probability map of the identification information detection device  2   b.    
     The hypothesis generation part  32  receives identifications, detection times, and identification detection device numbers from the identification information detection devices  2   a ,  2   b , thus holding their correlations.  FIG. 10  shows a table describing these correlations. This table describes which identification information detection device, specified by its identification detection device number, detects identification. Herein, the existence/nonexistence of a detected identification per each identification information detection device is shown in connection with times t 1  to t 9 . For instance, the identification information detection device  2   a  detects an identification ID 1  at times t 1 , t 2  whilst the identification information detection device  2   b  detects an identification ID 1  at times t 8 , t 9 . Blanks in the table of  FIG. 10  indicate that the identification information detection devices  2   a ,  2   b  do not detect identifications. 
       FIGS. 11 ,  12 ,  13 , and  14  are illustrations depicting the procedure for retrieving detection probabilities of identifications on the identification probability map of the tracking area  50 . 
     Next, the operation of the mobile body track identification device  3  will be described in detail. 
     First, the track-coupling candidate generation part  31  receives combinations of track numbers, detection times, positional coordinates from the positional information detection device  1  (step S 1 ). As shown in  FIG. 6  in which a plurality of coordinates is detected in a time sequence, the positional information detection device  1  sends a pair of { 1 , t 1 , p(30,80)} and { 2 , t 1 , p(25,10)} at time t 1 , for example, to the track-coupling candidate generation part  31  of the mobile body track identification device  3  as combinations of track numbers, detection times, and positional coordinates. The track-coupling candidate generation part  31  is provided with combinations of track numbers, detection times, and positional coordinates at times t 2 , t 3 , . . . after time t 1  as well. 
     The track-coupling candidate generation part  31  holds positional information which is sent from the positional information detection device  1  in a time range from a predetermined time in the past to the current time (i.e. the latest time for receiving information). Upon receiving the current combinations of track numbers, detection times, and positional coordinates in step S 1 , the track-coupling candidate generation part  31  additionally stores them as current positional information while deleting the oldest positional information, thus updating the history of positional information (step S 2 ). When the track-coupling candidate generation part  31  receives combinations of track numbers, detection times, and positional coordinates at time to while holding combinations of track numbers, detection times, and positional coordinates at times t 1  to tn- 1 , for example, it newly stores positional information at time to while discarding combinations of track numbers, detection times, and positional coordinates at time t 1 . 
     In positional information which the track-coupling candidate generation part  31  receives in a time range from a predetermined time in the past to the current time, a set of positional coordinates paired with the same track number represents a single track. After step S 2 , the track-coupling candidate generation part  31  combines positional information by using the track number as a key, thus generating and sending track-coupling candidates to the hypothesis generation part  32  (step S 3 ). Upon detecting tracks  1  to  4  shown in  FIG. 6 , the track-coupling candidate generation part  31  generates track-coupling candidate  1 =(track  1 , track  3 ), track-coupling candidate  2 =(track  2 , track  4 ), track-coupling candidate  3 =(track  1 , track  4 ), . . . . Provided N as the number of tracks, the track-coupling candidate generation part  31  generates all track-coupling candidates including a track-coupling candidate with a single track and a track-coupling candidate with N tracks. That is, the track-coupling candidate generation part  31  generates not only the foregoing track-coupling candidates each including two tracks, but also track-coupling candidates each including a single track such as (track  1 ), (track  2 ), . . . , track-coupling candidates each including three tracks such as (track  1 , track  2 , track  3 ), (track  1 , track  2 , track  4 ), . . . , and a track-coupling candidate including four tracks such as (track  1 , track  2 , track  3 , track  4 ). Additionally, it generates track-coupling candidates combining tracks which are detected at the same time. 
     Upon generating track-coupling candidates with positional coordinates of different tracks which are detected at the same time, the track-coupling candidate generation part  31  calculates positional coordinates of a median point among them as well. 
     Additionally, the track-coupling candidate generation part  31  does not only store combinations of track numbers, detection times, and positional coordinates (i.e. combinations of track numbers, detection times, and positional coordinates produced by the updating process of step S 2 ) detected in a predetermined time in the past by itself but also sends them to the likelihood calculation part  33 . Upon calculating a median point of positional information with positional coordinates of different tracks which are detected at the same time, it sends the median point to the likelihood calculation part  33  as well. The track-coupling candidate generation part  31  sends these pieces of information to the likelihood calculation part  33  via the hypothesis generation part  32 . 
     The hypothesis generation part  32  receives combinations of detection times, identifications, and identification detection device numbers from the identification information detection device  2  (step S 4 ). That is, upon detecting the identification of a mobile body, the identification information detection device  2  sends the identification with its relevant information to the hypothesis generation part  32 . The hypothesis generation part  32  stores the correlations between detection times, identifications, and identification detection device numbers in the table of  FIG. 10 . 
     The hypothesis generation part  32  holds identifications which are received from the identification information detection device  2  in a time range from a predetermined time in the past to the current time (i.e. the time of receiving current information). Upon receiving identifications and identification detection device numbers at the current detection time, the hypothesis generation part  32  additionally registers them with the table of  FIG. 10  while deleting the oldest identification (step S 5 ). Upon receiving combinations of detection times, identifications, and identification detection device numbers at the current time tn while holding combinations of detection times, identifications, and identification detection device numbers at times t 1  to tn- 1 , for example, the hypothesis generation part  32  registers the received identifications at time tn with the table of  FIG. 10  while discarding identifications at time t 1 . 
     The hypothesis generation part  32  stores the updated table content by itself while sending it to the likelihood calculation part  33 . 
     Using identifications stored in the table which is updated in step S 5  and track-coupling candidates received from the track-coupling candidate generation part  31  in step S 3 , the hypothesis generation part  32  generates track-coupling candidate/identification pairs combining them (step S 6 ). In examples shown in  FIGS. 6 and 10 , the hypothesis generation part  32  generates track-coupling candidate/identification pairs of {(track  1 , track  3 ), ID 1 }, {(track  2 , track  4 ), ID 1 }, . . . . Herein, each track-coupling candidate/identification pair includes two tracks, but the number of tracks included in each track-coupling candidate/identification pair is not limited to two. Additionally, the hypothesis generation part  32  generates track-coupling candidate/identification pairs including “unknown” indicating nonexistence of counterparts in step S 6 . Therefore, it generates track-coupling candidate/identification pairs of {(track  1 , track  3 ), unknown}, {(track  2 , track  4 ), unknown}, {(track  1 , track  4 ), unknown}, etc. 
     After step S 6 , the hypothesis generation part  32  generates all the sets of track-coupling candidate/identification pairs satisfying the first to third conditions (step S 7 ). In this connection, a single set of track-coupling candidate/identification pairs constitutes a hypothesis. The hypothesis generation part  32  sends hypotheses to the likelihood calculation part  33 . 
     The likelihood calculation part  33  makes a decision as to whether or not any hypothesis with uncalculated likelihoods exists in hypotheses which are produced in step S 7  (step S 8 ). If there exist hypotheses without calculations of likelihoods (specifically, the process from step S 9  to step S 11 ) (i.e. a decision result “Yes” in step S 8 ), the likelihood calculation control part  330  sends one of hypotheses with uncalculated likelihoods to the track-coupling likelihood calculation part  331  and the identification likelihood calculation part  332 . 
     Upon receiving hypotheses with uncalculated likelihoods, the track-coupling likelihood calculation part  331  calculates track-coupling likelihoods per each track-coupling candidate/identification pair ascribed to each of these hypotheses (step S 9 ). 
     In order to calculate a track-coupling likelihood between track  1  and track  3  shown in  FIG. 6 , a mobility between the disappearance of track  1  and the appearance of track  3  is calculated based on the disappearance time t 4  of track  1  and the appearance time t 6  of track  3  as well as positional coordinates of disappearance p(90,60) of track  1  and positional coordinates of appearance p(130,55) of track  3 . An error between the mobility and the pre-defined mobility is calculated and used as a parameter for a likelihood function, thus producing a track-coupling likelihood. In this connection, the calculation method of track-coupling likelihoods is not limited to this method; hence, it is possible to employ other methods. 
     Since the calculation method of track-coupling likelihoods regarding tracks which are detected at the same time and the calculation method of track-coupling likelihoods regarding three or more tracks have been already described, their descriptions will be omitted in this paragraph. In the calculation of step S 9 , the track-coupling candidate generation part  31  may refer to combinations of positional information, detection times, and track numbers, which are detected in a predetermined time in the past, provided by the track-coupling candidate generation part  331 . 
     In step S 9 , the track-coupling likelihood calculation part  331  calculates track-coupling likelihoods, the number of which corresponds to the number of track-coupling candidate/identification pairs ascribed to each hypothesis, since it calculates track-coupling likelihoods per each track-coupling candidate/identification pair ascribed to a hypothesis. 
     Upon receiving hypotheses with uncalculated likelihoods, the identification likelihood calculation part  332  calculates identification likelihoods with respect to these hypotheses (step S 10 ). In step S 10 , the identification likelihood calculation part  332  calculates identification likelihoods per each track-coupling candidate/identification pair ascribed to a hypothesis and then normalizes the multiplication result, thus providing its normalization value as an identification likelihood. However, the identification likelihood calculation part  332  does not calculate identification likelihoods with respect to track-coupling candidate/identification pairs with track-coupling candidates or identifications indicating “unknown”; hence, it solely multiplies and normalizes identification likelihoods, which are calculated with respect to other track-coupling candidate/identification pairs. 
     The operation of the identification likelihood calculation part  332  calculating identification likelihoods of hypotheses will be described with regard to an example of [{(track  1 , track  3 ), ID 1 }, {(track  2 , track  4 ), unknown}]. Herein, it is presumed that the relationship between identifications, detection times, and identification detection device numbers shown in  FIG. 10  has already been produced. The identification likelihood calculation part  332  extracts track-coupling candidate/identification pairs from a hypothesis so as to implement the following procedure on track-coupling candidate/identification pairs without including “unknown”. 
     First, the identification likelihood calculation part  332  extracts identifications from track-coupling candidate/identification pairs. For instance, the identification likelihood calculation part  332  extracts ID 1  from {(track  1 , track  3 ), ID 1 }. Next, the identification likelihood calculation part  332  reads a detection probability of the extracted identification from an identification probability map. As shown in  FIG. 10 , it can be understood that the identification information detection device  2   a  has detected the identification ID 1  at times t 1 , t 2 . Therefore, the identification likelihood calculation part  332  reads probabilities from the identification probability map of the identification information detection device  2   a  (see  FIG. 8 ) at times t 1 , t 2 . As shown in  FIG. 11 , a mobile body exists at cells c(1,4), c(2,3) at times t 1 , t 2  in connection with a track-coupling candidate (track  1 , track  3 ). As a probability at time t 1 , the identification likelihood calculation part  332  reads a value “0.8” of the cell c(1,4) from the identification probability map shown in  FIG. 8 . As a probability at time t 2 , it reads a value “0.5” of the cell c(2,3). To implement this procedure, the identification likelihood calculation part  332  may refer to combinations of positional information, detection times, and track numbers, which are detected in a predetermined time in the past, provided by the track-coupling candidate generation part  31  as well as the table (i.e. the table after updating in step S 5 ) provided by the hypothesis generation part  32 . 
     With reference to  FIG. 10 , it is understood that the identification information detection device  2   b  has detected the identification ID 1  at times t 8 , t 9 . The identification likelihood calculation part  332  reads probabilities at times t 8 , t 9  from the identification probability map of the identification information detection device  2   b  (see  FIG. 9 ). As shown in  FIG. 12 , a mobile body exists in cells c(7,1), c(7,0) a times t 8 , t 9 . Therefore, the identification likelihood calculation part  332  reads a value “0.7 of the cell c(7,1) from the identification probability map of  FIG. 9  as a probability at time t 8 . Additionally, it reads a value “0.8” of the cell c(7,0) as a probability at time t 9 . 
     As described above, the identification likelihood calculation part  332  determines a detection probability of a mobile body at each time of detecting its identification based on positional information specified by a track-coupling candidate and an identification probability map specified by the identification detection device number. In this connection, it is unnecessary to determine detection probabilities of a mobile body at timings of detecting no identifications and at times when the position of a mobile body, located outside the tracking area, cannot be detected. 
     The identification likelihood calculation part  332  multiplies detection probabilities of a mobile body, which are determined based on positional information specified by track-coupling candidates and identification probability maps specified by identification detection device numbers at their times, thus producing an identification likelihood with respect to a track-coupling candidate/identification pair which is currently being processed. It multiplies the foregoing probabilities 0.8, 0.5, 0.7, and 0.8 with respect to {(track  1 , track  3 ), ID 1 }. That is, it produces 0.8×0.5×0.7×0.8=0.224 as an identification likelihood for {(track  1 , track  3 ), ID 1 }. Additionally, the identification likelihood calculation part  332  accumulates the numbers of probabilities used for multiplication per each track-coupling candidate/identification pair, thus producing an accumulation result M. 
     In the foregoing hypothesis [{(track  1 , track  3 ), ID 1 }, {(track  2 , track  4 ), unknown}], an identification of “unknown” is included in {(track  2 , track  4 ), unknown}, in which its track-coupling candidate is not correlated to its identification. Therefore, the identification likelihood calculation part  332  does not calculate an identification likelihood with respect to {(track  2 , track  4 ), unknown}. 
     The identification likelihood calculation part  332  normalizes the multiplication result of probabilities which are calculated per each track-coupling candidate/identification pair ascribed to a hypothesis, thus providing a normalization value as an identification likelihood for the hypothesis. The foregoing hypothesis produces an identification “0.224” with respect to {(track  1 , track  3 ), ID 1 }; hence, this identification should be normalized. Normalization produces an Mth root of the multiplication result of identification likelihoods which are calculated per each track-coupling candidate/identification pair. Herein, M indicates an accumulating value of probabilities (i.e. their total number) used for calculating identification likelihoods per each track-coupling candidate/identification pair. Since four probabilities are used to calculate an identification likelihood of “0.224” with respect to {(track  1 , track  3 ), ID 1 }, the identification likelihood calculation part  332  carries out normalization to calculate a quadruple root of “0.224”, thus determining the normalization result as an identification likelihood representing the entirety of the hypothesis [{(track  1 , track  3 ), ID 1 }, {(track  2 , track  4 ), unknown}]. 
     Next, normalization regarding an identification likelihood for another hypothesis [{(track 1 ), ID 1 }, {(track  2 , track  3 , track  4 ), unknown}] will be described with reference to  FIGS. 6 and 10 . Two probabilities are read from coordinates of track  1  at times t 1 , t 2  with respect to {(track  1 ), ID 1 }. No identification likelihood is calculated with respect to {(track  2 , track  3 , track  4 ), unknown}. That is, the identification likelihood calculation part  332  calculates an identification likelihood of {(track  1 ), ID 1 } and then carries out normalization to calculate its square root. Additionally, no identification likelihood is calculated with respect to {(track  1 ), unknown} including “unknown” in another hypothesis [{(track  1 ), unknown}, {(track  2 , track  3 , track  4 ), ID 1 }]. On the other hand, two probabilities are read from coordinates of track  2  at times t 1 , t 2  with respect to {(track  2 , track  3 , track  4 ), ID 1 }. A single probability is read from coordinates of a median point between track  3  and track  4  at time t 8 . A single probability is read from coordinates of a median point between track  3  and track  4  at time t 9 . That is, it is possible to obtain four probabilities in total. Therefore, the identification likelihood calculation part  332  calculates an identification likelihood with respect to {(track  2 , track  3 , track  4 ), ID 1 } and then carries out normalization to calculate its quadruple root. 
     Next, a calculation process regarding an identification likelihood of a hypothesis [{(track  1 , track  3 ), unknown}, {(track  2 , track  4 ), ID 1 }] will be described. Herein, no identification likelihood is calculated with respect to {(track  1 , track  3 ), unknown} including “unknown”. On the other hand, the identification likelihood calculation part  332  extracts an identification ID 1  from {(track  2 , track  4 ), ID 1 }. The identification information detection device  2   a  has detected the identification ID 1  at times t 1 , t 2  (see  FIG. 10 ). The identification likelihood calculation part  332  reads probabilities at times t 1 , t 2  from the identification probability map of the identification information detection device  2   a  (see  FIG. 8 ). As shown in  FIG. 13 , a mobile body exists in cells c(1,0), c(1,1) at times t 1 , t 2  in connection with a track-coupling candidate (track  2 , track  4 ). Therefore, the identification likelihood calculation part  332  reads a value “0.1” of the cell c(1,0) from the identification probability map of  FIG. 8  as a probability at time t 1 . Additionally, it reads a value “0.3” of the cell c(1,1) as a probability at time t 2 . 
     The identification information detection device  2   b  has detected the identification ID 1  at times t 8 , t 9  (see  FIG. 10 ). The identification likelihood calculation part  332  reads probabilities at times t 8 , t 9  from the identification probability map of the identification information detection device  2   b  (see  FIG. 9 ). On the other hand, a mobile body exists in the cells c(7,1), c(7,0) at times t 8 , t 9  in connection with a track-coupling candidate (track  2 , track  4 ) as shown in  FIG. 14 . Therefore, the identification likelihood calculation part  332  reads a value “0.7” of the cell c(7,1) from the identification probability map of  FIG. 9  as a probability at time t 8 . Additionally, it reads a value “0.8” of the cell c(7,0) as a probability at time t 9 . 
     The identification likelihood calculation part  332  determines the multiplication result of probabilities 0.1, 0.3, 0.7, and 0.8, which are calculated with respect to {(track  2 , track  4 ), ID 1 }, as an identification likelihood. That is, the identification likelihood of {(track  2 , track  4 ), ID 1 } is 0.1×0.3×0.7×0.8=0.0168. This calculation uses four probabilities. Since no identification likelihood is calculated with respect to {(track  1 , track  3 ), unknown} including “unknown”, the identification likelihood calculation part  332  carries out normalization to calculate a quadruple root of 0.0168. The normalization result is determined as the identification likelihood of the hypothesis [{(track  1 , track  3 ), unknown}, {(track  2 , track  4 ), ID 1 }]. 
     After step S 10  for calculating the identification likelihood of a hypothesis, the identification likelihood calculation part  332  integrates the track-coupling likelihood (i.e. the track-coupling likelihood per each track-coupling candidate/identification pair ascribed to a hypothesis), calculated in step S 9 , and the identification likelihood calculated in step S 10  (step S 11 ). Herein, the identification likelihood calculation part  332  integrates likelihoods by multiplying the track-coupling likelihood and the identification likelihood. That is, the multiplication result of the track-coupling likelihood and the identification likelihood is used as an integrated result of likelihoods. In this case, the identification likelihood calculation part  332  may perform a weighted calculation on the track-coupling likelihood and the identification likelihood. For instance, the track-coupling likelihood and the identification likelihood can be multiplied by weight coefficients and then multiplied together. The identification likelihood calculation part  332  stores a pair of a hypothesis and an integrated result of likelihoods. When X denotes an integrated result of likelihoods as [{(track  1 , track  3 ), ID 1 }, {(track  2 , track  4 ), unknown}], for example, a pair of &lt;[{(track  1 , track  3 ), ID 1 }, {(track  2 , track  4 ), unknown}], X&gt; is stored. 
     In step S 9 , it is possible to produce a plurality of track-coupling likelihoods, the number of which corresponds to the number of track-coupling candidate/identification pairs ascribed to a hypothesis. Herein, it is possible to integrate a plurality of track-coupling likelihoods which are calculated by the track-coupling likelihood calculation part  331 . That is, the track-coupling likelihood calculation part  331  multiplies a plurality of track-coupling likelihoods. In step S 11 , the multiplication result of track-coupling likelihoods is multiplied by an identification likelihood. That is, multiplication of track-coupling likelihoods can be carried out in step S 9 , or it can be carried out in step S 11  together with integration with an identification likelihood. In either case, step S 11  finally produces the same integration result of likelihoods. 
     After step S 11 , the likelihood calculation part  33  repeatedly executes a loop consisting of steps S 8  to S 11 . Upon determining nonexistence of hypotheses with uncalculated likelihoods in step S 8  (i.e. a decision result “No” of step S 8 ), the identification likelihood calculation part  332  specifies a hypothesis providing the maximum integration result of the track-coupling likelihood and the identification likelihood (step S 12 ). That is, the identification likelihood calculation part  332  refers to pairs of hypotheses and integration results of likelihoods stored in step S 11  so as to specify a hypothesis providing the maximum integration result of likelihoods. The identification likelihood calculation part  332  sends the most-probable hypothesis to the identification result output device  4 . 
     Upon receiving the most-probable hypothesis specified in step S 12 , the identification result output device  4  determines which mobile body matches which track based on track-coupling candidate/identification pairs ascribed to the most-probable hypothesis. For instance, the relationship between tracks and mobile bodies are displayed (step S 13 ). At this time, the identification result output device  4  may specifically displays the position or track of a mobile body. 
     The operation of Embodiment can be summarized such that the track-coupling candidate generation part  31  generates track-coupling candidates combining tracks which are detected in a predetermined time in the past while the hypothesis generation part  32  generates hypotheses representing sets of pairs of track-coupling candidates and identifications. With reference to pairs of positional information and identifications which are detected at the same time, the likelihood calculation part  33  calculates similarities of positional information per each hypothesis, based on positional information and identifications which are detected in a predetermined time in the past, as likelihoods. Pairs of positional information and identifications which are detected at the same time may indicate identity information with many mobile bodies which are detected in a predetermined time in the past. This makes it possible to identify and track a mobile body without solely depending on the position which is confirmed as the existence of a mobile body; hence, it is possible to robustly estimate the correlation between tracks and identifications irrespective of interruption of tracking. That is, it is possible to robustly estimate which mobile body matches which track. 
     The technologies disclosed in Patent Documents 1, 2 are designed to resume tracking based on newly reacquired features and identifications when interruption frequently occurs in tracking mobile bodies; in other words, correlating and tracking of mobile bodies are resumed based on a small amount of information which is newly acquired after interruption of tracking. For this reason, they do not expect high-precision tracking of mobile bodies. In contrast, Embodiment 1 is designed to generate track-coupling candidates connecting fragmented tracks irrespective of interruption in tracking mobile bodies, thus estimating the correlations between tracks and identifications based on the information which is detected in a predetermined time in the past (i.e. information determined in step S 2 , and information shown in  FIG. 10 ). As described above, Embodiment 1 utilizes not only the information recognized at the present time but also the information which is collected in a time range retroactive to the past by a predetermined time; hence, it is possible to estimate the correlations between tracks and identifications with a high precision. 
     Next, the mobile body track identification method of Embodiment 1 is compared with the conventional mobile body track identification method which utilizes only the information after resuming tracking without using the information before occurrence of interruption when interruption occurs in tracking mobile bodies. 
     According to the conventional mobile body track identification method, when interruption of tracking occurs at times t 4  to t 6  as shown in  FIGS. 1 to 14 , it is impossible to establish the correlation between tracks and identifications with respect to track  3  and track  4 , which are detected thereafter, until a new identification is detected at time t 8 . As shown in  FIGS. 11 to 14 , track  3  and track  4  are positioned in the same cell at times t 8 , t 9 . In this case, it is difficult to determine which of track  3  and track  4  should be correlated to the identification detected at time t 8 . 
     In contrast, the mobile body track identification method of Embodiment 1 connects tracks by use of the information detected at times t 1  to t 9 , and calculates identification likelihoods based on detection probabilities of identifications at detection times in a predetermined time in the past, thus estimating most-probable hypotheses. 
     Next, a variation of Embodiment 1 will be described. Herein, the identification likelihood calculation part  332  calculates integration results of track-coupling likelihoods and identification likelihoods in step S 11  so as to store pairs of hypotheses and integration results of likelihoods. Additionally, the identification likelihood calculation part  332  estimates a hypothesis having the highest integration result of likelihoods as a most-probable hypothesis in step S 12 . That is, it is possible to specify hypotheses solely based on identification likelihoods without calculating integration results of track-coupling likelihoods and identification likelihoods. According to this variation, the likelihood calculation part  33  does not need to include the track-coupling likelihood calculation part  331 ; hence, it is unnecessary to calculate track-coupling likelihoods (step S 9 ). Since track-coupling likelihoods need not be calculated, it is unnecessary to integrate track-coupling likelihoods and identification likelihoods (step S 11 ). According to this variation, when the existence of hypotheses with uncalculated likelihoods is determined (i.e. a decision result “Yes” of step S 8 ) so that the identification likelihood calculation part  332  calculates identification likelihoods of hypotheses in step S 10 , it is necessary to store pairs of hypotheses and identification likelihoods. Subsequently, the identification likelihood calculation part  332  may specify a hypothesis having the highest identification likelihood in step S 12 . Other processes are similar to those of Embodiment 1. 
     However, it is preferable that the most-probable hypothesis be estimated based on integration results of track-coupling likelihoods and identification likelihoods because this may improve a precision in estimating the correlation between tracks and identifications. 
     Embodiment 2 
       FIG. 15  is a block diagram of a mobile body track identification system according to Embodiment 2 of the present invention. Herein, the same constituent elements as the constituent elements of Embodiment 1 (see  FIG. 2 ) are designated by the same reference numerals; hence, detailed descriptions thereof will be omitted. The mobile body track identification system of Embodiment 2 includes the positional information detection device  1 , the identification information detection device  2 , a mobile body track identification device  3   a , and the identification result output device  4 . The mobile body track identification device  3   a  includes the track-coupling candidate generation part  31 , the hypothesis generation part  32 , and the map storage  37 . Additionally, the mobile body track identification device  3   a  includes a likelihood calculation part  33   a  and a track-coupling likelihood calculation part  331   a.    
     The track-coupling likelihood calculation part  331   a  calculates a track-coupling likelihood per each track-coupling candidate generated by the track-coupling candidate generation part  31 . The calculation method of track-coupling likelihoods of the track-coupling likelihood calculation part  331   a  is identical to that of the track-coupling likelihood calculation part  331  of Embodiment 1. That is, the track-coupling likelihood calculation part  331   a  of Embodiment 2 solely performs calculation on track-coupling candidates including tracks which are detected at different times irrespective of track-coupling candidates including tracks which are detected at the same time. Additionally, the track-coupling likelihood calculation part  331   a  of Embodiment 2 performs calculations on track-coupling candidates each including two tracks and track-coupling candidates each including three or more tracks. Moreover, the track-coupling likelihood calculation part  331   a  of Embodiment 2 sets a constant (e.g. “1”) to track-coupling likelihoods of track-coupling candidates each including a single track. 
     The track-coupling likelihood calculation part  331   a  solely sends track-coupling candidates whose track-coupling likelihoods, calculated by the track-coupling likelihood calculation part  331   a , are above or equal to a threshold to the hypothesis generation part  32 . Therefore, it precludes track-coupling candidates with track-coupling likelihoods below the threshold from processing. Herein, the threshold for track-coupling likelihoods is determined in advance. Additionally, the track-coupling likelihood calculation part  331   a  may solely send track-coupling candidates whose track-coupling likelihoods exceed the threshold to the hypothesis generation part  32 . 
     The hypothesis generation part  32  generates hypotheses by use of track-coupling candidates received from the track-coupling likelihood calculation part  331   a . Other processes of the hypothesis generation part  32  are identical to those of Embodiment 1. 
     The likelihood calculation part  33   a  of Embodiment 2 calculates identification likelihoods per each hypothesis generated by the hypothesis generation part  32 , thus sending a hypothesis with the highest identification likelihood to the identification result output device  4 .  FIG. 16  is a block diagram showing the constitution of the likelihood calculation part  33   a . Herein, the same constituent parts as the constituent parts of the likelihood calculation part  33  of Embodiment 1 (see  FIG. 3 ) are designated by the same reference numerals. The likelihood calculation part  33   a  includes the likelihood calculation control part  330  and the identification likelihood calculation part  332  but does not include the track-coupling likelihood calculation part  331  used in Embodiment 1. That is, the likelihood calculation part  33   a  of Embodiment 2 differs from the likelihood calculation part  33  of Embodiment 1 in that it does not calculate track-coupling likelihoods. 
     In the likelihood calculation part  33   a  of Embodiment 2, the likelihood calculation control part  330  sends hypotheses with uncalculated identification likelihoods to the identification likelihood calculation part  332 . 
     Similar to Embodiment 1, the identification likelihood calculation part  332  calculates identification likelihoods of track-coupling candidate/identification pairs per each hypothesis. In Embodiment 2, however, the identification likelihood calculation part  332  sends a hypothesis with the highest identification likelihood to the identification result output device  4 . In Embodiment 2, the identification likelihood calculation part  332  specifies a hypothesis to be sent to the identification result output device  4  based on identification likelihoods rather than the integration result of track-coupling likelihoods and identification likelihoods. 
     For instance, the track-coupling candidate generation part  31 , the track-coupling likelihood calculation part  331   a , the hypothesis generation part  32 , and the likelihood calculation part  33   a  (including the likelihood calculation control part  330  and the identification likelihood calculation part  332 ) are realized using a computer which operates according to a mobile body track identification program. That is, a program memory of a computer (not shown) stores the mobile body track identification program so that a CPU loads the program, thus realizing the functions of the track-coupling candidate generation part  31 , the track-coupling likelihood calculation part  331   a , the hypothesis generation part  32 , and the likelihood calculation part  33   a . Alternatively, it is possible to realize the track-coupling candidate generation part  31 , the track-coupling candidate calculation part  331   a , the hypothesis generation part  32 , and the likelihood calculation part  33   a  by use of individual hardware elements. Additionally, the likelihood calculation control part  330  and the identification likelihood calculation part  332  can be realized by use of individual hardware elements. 
     Next, the operation of the mobile body track identification device  3   a  of the mobile body track identification system of Embodiment 2 will be described. 
       FIGS. 17 and 18  are flowcharts showing the processing of the mobile body track identification device  3   a  of Embodiment 2. First, the track-coupling candidate generation part  31  retrieves combinations of track numbers, detection times, and positional coordinates from the positional information detection device  2  (step S 21 ). 
     Upon receiving the current combination of positional information, detection times, and track numbers in step S 21 , the track-coupling candidate generation part  31  additionally store it therein while deleting the oldest positional information, thus updating the positional information (step S 22 ). As a result, the track-coupling candidate generation part  31  holds a plurality of positional information which is detected in a predetermined time in the past from the current time. The processes of steps S 21  and S 22  of Embodiment 2 are identical to those of Embodiment 1. 
     Next, the track-coupling candidate generation part  31  generates and sends track-coupling candidate, combining track numbers held therein, to the track-coupling likelihood generation part  331   a  (step S 23 ). The track-coupling candidate generation part  31  calculates positional coordinates of a median point among different positional coordinates of tracks which are detected at the same time with respect to each track-coupling candidate. Calculations of track-coupling candidates and positional coordinates of median points in Embodiment 3 are identical to those of step S 3  in Embodiment 1. 
     The track-coupling candidate generation part  31  does not only store combinations of positional information, detection times, and track numbers (i.e. combinations of positional information, detection times, and track numbers determined by an updating process of step S 22 ), which are detected in a predetermined time in the past, by itself but also sends them to the track-coupling likelihood calculation part  331   a  and the likelihood calculation part  33   a . It calculates and sends a median point of different positional coordinates of tracks, which are detected at the same time, to the likelihood calculation part  33   a  as well. These pieces of information can be supplied to the likelihood calculation part  33   a  via the track-coupling likelihood calculation part  331   a  and the hypothesis generation part  32 . 
     The track-coupling likelihood calculation part  331   a  calculates a track-coupling likelihood per each track-coupling candidate received in step S 23  (step S 24 ). In Embodiment 2, the process of calculating track-coupling likelihoods for individual track-coupling candidates is identical to the calculation process of track-coupling likelihoods of the track-coupling likelihood calculation part  331  of Embodiment 1. In step S 24 , the track-coupling likelihood calculation part  331   a  compares track-coupling likelihoods with a predetermined threshold so as to solely send track-coupling candidates whose track-coupling likelihoods are above or equal to the threshold to the hypothesis generation part  32 . 
     The hypothesis generation part  32  retrieves combinations of a detection times, identifications, and identification detection device numbers from the identification information detection device  2  (step S 25 ). The hypothesis generation part  32  additionally registers combinations of detection times, identifications, and identification detection device numbers with the table of  FIG. 10  while deleting the oldest identification (step S 26 ). The hypothesis generation part  32  does not only store the updated table content by itself but also sends it to the likelihood calculation part  33   a . In this connection, steps S 25 ,  26  of Embodiment 2 are identical to steps S 4 , S 5  of Embodiment 1 (see  FIG. 4 ). 
     The hypothesis generation part  32  generates all the sets of track-coupling candidate/identification pairs by use of identifications stored in the table updated in step S 26  and track-coupling candidates received from the track-coupling likelihood calculation part  331   a  in step S 24  (step S 27 ). Step S 27  of Embodiment 2 is similar to step S 26  of Embodiment except that it uses track-coupling candidates received from track-coupling candidates received from the track-coupling likelihood calculation part  331   a.    
     Next, the hypothesis generation part  32  generates all the sets of track-coupling candidate/identification pairs (i.e. hypotheses) satisfying the first to third conditions (step S 28 ). Step S 28  of Embodiment 2 is identical to step S 7  of Embodiment 1. The hypothesis generation part  32  sends hypotheses, generated therein, to the likelihood calculation part  33   a.    
     The likelihood calculation control part  330  of the likelihood calculation part  33   a  makes a decision as to whether or not any hypothesis with uncalculated likelihoods (i.e. non-execution of step S 30 ) exists in hypotheses received in step S 28  (step S 29 ). When hypotheses with uncalculated likelihoods exists (i.e. a decision result “Yes” of step S 29 ), the identification likelihood calculation part  332  sends one of hypotheses with uncalculated likelihoods to the identification likelihood calculation part  332 . 
     The identification likelihood calculation part  332  calculates an identification likelihood of the received hypothesis (step S 30 ). The calculation process of identification likelihoods of hypotheses in Embodiment 2 is identical to Embodiment 1. That is, the identification likelihood calculation part  332  calculates identification likelihoods per each track-coupling candidate/identification pair ascribed to a hypothesis, multiplies and normalizes identification likelihoods per each track-coupling candidate/identification pair, thus determining an identification likelihood of a hypothesis. In step S 30  of Embodiment 2, the identification likelihood calculation part  332  stores identification likelihoods in connection with hypotheses. 
     After step S 30 , the likelihood calculation part  33   a  repeatedly executes steps S 29  and S 30 . Upon determining nonexistence of hypotheses with uncalculated likelihoods in step S 29  (i.e. a decision result “No” of step S 29 ), the identification likelihood calculation part  332  estimates a hypothesis having the highest identification likelihood as a most-probable hypothesis (step S 31 ). The identification likelihood calculation part  332  sends the most-probable hypothesis to the identification result output device  4 . 
     Upon receiving the most-probable hypothesis in step S 31 , the identification result output part  4  determines and presents which mobile body matches which track based on track-coupling candidate/identification pairs ascribed to the most-probable hypothesis (step S 32 ). Herein, the identification result output device  4  may specifically display the position or track of a mobile body. In this connection, step S 32  of Embodiment 2 is identical to step S 13  of Embodiment 1 (see  FIG. 5 ). 
     Similar to Embodiment 1, Embodiment 2 is able to robustly estimate the correlation between tracks and identifications. 
     In Embodiment 2, the track-coupling likelihood calculation part  331   a  calculates track-coupling likelihoods of track-coupling candidates so as not to provide the hypothesis generation part  32  with track-coupling candidates whose track-coupling likelihoods are less than the threshold, thus precluding those track-coupling candidates from being used for hypothesis generation. Thus, it is possible to reduce workloads of the hypothesis generation part  32  in terms of the process of generating track-coupling candidate/identification pairs (step S 27 ) and the process of generating hypotheses (step S 28 ). As a result, it is possible to establish the correlation between tracks and identifications at a high speed. That is, it is possible to determine which mobile body matches which track at a high speed. 
     Embodiment 3 
       FIG. 19  is a block diagram of a mobile body track identification system according to Embodiment 3 of the present invention. Herein, the same constituent parts as the constituent parts of Embodiment 1 are designated by the same reference numerals; hence, detailed descriptions thereof will be omitted. The mobile body track identification system of Embodiment 3 includes the positional information detection device  1 , the identification information detection device  2 , a mobile body track identification device  3   b , and the identification result output device  4 . The mobile body track identification device  3   b  of Embodiment 3 includes the track-coupling candidate generation part  31 , the hypothesis generation part  32 , and the map storage  37 . Additionally, the mobile body track identification device  3   b  includes a likelihood calculation part  33   b  and a likelihood storage  24 . 
     The likelihood storage  34  stores a likelihood of correlating tracks and identifications (hereinafter, referred to as a “track/identification correlation likelihood”) per each pair of an identification and a track number representing a track to be followed. The likelihood storage  34  stores a track/identification correlation likelihood per each pair of a track number and an identification. The likelihood calculation part  33   b  calculates track/identification correlation likelihoods, which are stored in the likelihood storage  34 . 
     The likelihood calculation part  33   b  calculates and integrates track-coupling likelihoods and identification likelihoods per each hypothesis. Additionally, the likelihood calculation part  33   b  estimates a hypothesis with the highest integration value of track-coupling likelihoods and identification likelihoods as a most-probable hypothesis, thus sending it to the identification result output device  4 . 
     In order to calculate identification likelihoods of track-coupling candidate/identification pairs ascribed to a hypothesis, the likelihood calculation part  33   b  calculates identification likelihoods in a similar way of Embodiment 1, and integrates them with track/identification correlation likelihoods stored in the likelihood storage  34 , thus determining their integration values as identification likelihoods of track-coupling candidate/identification pairs. Specifically, the likelihood calculation part  33   b  reads a track/identification correlation likelihood, stored in the likelihood storage  34 , with respect to the current combination of a track (a track number) and its identification, corresponding to a track-coupling candidate of a track-coupling candidate/identification pair, thus providing it to the above integration process. The likelihood calculation part  33   b  multiplies and normalizes identification likelihoods of track-coupling candidate/identification pairs, thus calculating identification likelihoods of hypotheses. 
     The likelihood calculation part  33   b  calculates a track/identification correlation likelihood based on an identification likelihood per each track-coupling candidate/identification pair ascribed to each hypothesis, thus storing it in the likelihood storage  34 . Specifically, an identification likelihood (called Q) is stored as a track/identification correlation likelihood in the likelihood storage  34  in connection with a combination of an identification (called a) of a track-coupling candidate/identification pair and a current track (a track number β) of its track-coupling candidate. This indicates that a mobile body having the identification α is correlated to a track having the track number β with the probability Q. As described above, a track/identification correlation likelihood stored in the likelihood storage  34  is read in a next cycle of the likelihood calculation  33   b  that calculates an identification likelihood of a track-coupling candidate/identification pair ascribed to a hypothesis. 
       FIG. 20  is a block diagram showing the constitution of the likelihood calculation part  33   b  of Embodiment 3. The likelihood calculation part  33   b  includes the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , and an identification likelihood calculation part  332   b . The likelihood calculation control part  330  and the track-coupling likelihood calculation part  331  of Embodiment 3 are identical to those of Embodiment 1; hence, their descriptions will be omitted. 
     The identification likelihood calculation part  332   b  calculates identification likelihoods per each hypothesis. Before calculating an identification likelihood per each track-coupling candidate/identification pair ascribed to a hypothesis, the identification likelihood calculation part  332   b  temporarily calculates an identification likelihood according to the same calculation method of Embodiment 1 at first. For the sake of convenience, this identification likelihood will be referred to as a temporary identification likelihood. The identification likelihood calculation part  332   b  reads a track/identification correlation likelihood from the likelihood storage  34  with respect to a combination of the current track and the identification of a track-coupling candidate/identification par which is currently focused on. When track  2  represents the current track in {(track  1 , track  2 ), ID 1 }, for example, a track/identification correlation likelihood regarding “track  2 , ID 1 ” is read from the likelihood storage  34 . The identification likelihood calculation part  332   b  determines the integration result of the temporary identification likelihood and the track/identification correlation likelihood as an identification likelihood of the track-coupling candidate/identification pair which is currently focused on. 
     The identification likelihood calculation part  332   b  performs the following integration process with the temporary identification likelihood and the track/identification correlation likelihood. That is, the identification likelihood calculation part  332   b  multiplies the temporary identification likelihood and the track/identification correlation likelihood so as to determine the multiplication result as an identification likelihood of the track-coupling candidate/identification pair. Alternatively, the identification likelihood calculation part  332   b  multiplies the temporary identification likelihood and the track/identification correlation likelihood by weight coefficients and then multiplies them together, thus determining the multiplication result as an identification likelihood of the track-coupling candidate/identification pair. Alternatively, the identification likelihood calculation part  332   b  multiplies the temporary identification likelihood and the track/identification correlation likelihood by weight coefficients and the adds them together, thus determining the addition result as an identification likelihood of the track-coupling candidate/identification pair. 
     The identification likelihood calculation part  332   b  multiples and normalizes identification likelihoods of track-coupling candidate/identification pairs so as to calculate an identification likelihood representing the entirety of a hypothesis, thus integrating it with the track-coupling likelihood. The foregoing process of Embodiment 3 is identical to that of Embodiment 1. The identification likelihood calculation part  332   b  estimates a hypothesis with the highest integration value as a most-probable hypothesis so as to send it to the identification result output device  4 . 
     The integration likelihood calculation part  332   b  calculates integration values of track-coupling likelihoods and identification likelihoods with respect to all the hypotheses, thus estimating a most-probable hypothesis. Thereafter, it calculates track/identification correlation likelihoods with respect to track-coupling candidate/identification pairs ascribed to individual hypotheses, thus storing them in the likelihood storage  34 . 
     In Embodiment 3, the track-coupling candidate generation part  31 , the hypothesis generation part  32 , and the likelihood calculation part  33   b  (including the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , and the identification likelihood calculation part  332   b ) can be realized by means of a CPU of a computer that operates according to a mobile body track identification program. In this case, a program memory of a computer (not shown) stores the mobile body track identification program so that the CPU loads the program so as to realize the functions of the track-coupling candidate generation part  31 , the hypothesis generation part  32 , and the likelihood calculation part  33   b . Alternatively, the track-coupling candidate generation part  31 , the hypothesis generation part  32 , and the likelihood calculation part  33   b  can be realized using individual hardware elements. Similarly, the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , and the identification likelihood calculation part  332   b  can be realized using individual hardware elements. 
     Next, the operation of the mobile body track identification device  3   b  of Embodiment 3 will be described. 
       FIGS. 21 and 22  are flowcharts showing the processing of the mobile body track identification device  3   b  of Embodiment 3. Herein, the same steps as the steps of the mobile body track identification device  3  of Embodiment 1 are designated by the same reference numerals shown in the flowcharts of  FIGS. 4 and 5 ; hence, descriptions thereof will be omitted. The process for generating hypotheses in steps S 1  to S 7  in  FIG. 21  is identical to that in steps S 1  to S 7  in  FIG. 4 . In step S 7 , however, it is possible to generate hypotheses satisfying the first to third conditions described in Embodiment 1. This is because identification likelihoods are not calculated with respect to hypotheses not satisfying the third condition, whereas Embodiment 3 is able to calculate likelihoods of hypotheses with reference to track/identification correlation likelihoods stored in the likelihood storage  34 . 
     The hypothesis generation part  32  sends all the generated hypotheses to the likelihood calculation part  33   b . The likelihood calculation control part  330  of the likelihood calculation part  33   b  makes a decision as to whether or not any hypothesis with uncalculated likelihoods exists in the received hypotheses (step S 8 ). When hypotheses with uncalculated likelihoods (i.e. hypotheses without execution of steps S 9  to S 11 ) exist (i.e. a decision result “Yes” of step S 8 ), the likelihood calculation control part  330  sends one of hypotheses with uncalculated likelihoods to the track-coupling likelihood calculation part  331  and the identification likelihood calculation part  332   b.    
     Upon receiving a hypothesis without uncalculated likelihoods, the track-coupling likelihood calculation part  331  calculates a track-coupling likelihood per each track-coupling candidate/identification pair ascribed to the hypothesis (step S 9 ). The step S 9  of Embodiment 3 are identical to step S 9  of Embodiment 1. 
     The identification likelihood calculation part  332   b  calculates a temporary identification likelihood per each track-coupling candidate/identification pair ascribed to a hypothesis with uncalculated likelihoods (step S 10   a ). The method for calculating a temporary identification likelihood per each track-coupling candidate/identification pair in Embodiment 3 is identical to the method for calculating a temporary identification likelihood per each track-coupling candidate/identification pair in step S 10  of Embodiment 1 (see  FIG. 5 ). Since Embodiment 3 performs further calculation on the calculation result of step S 10   a  so as to produce an identification likelihood per each track-coupling candidate/identification pair; hence, the calculated value of step S 10   a  is called a “temporary identification likelihood”. 
     Next, the identification likelihood calculation part  332   b  reads a track/identification correlation likelihood per each track-coupling candidate/identification pair from the likelihood storage  34 . This track/identification correlation likelihood is a probability value which is stored in connection with a pair of a track number and an identification corresponding to the current track per each tack-coupling candidate/identification pair. Then, the track/identification correlation likelihood is integrated with the temporary identification likelihood which is calculated in step S 10   a  per each track-coupling candidate/identification pair (step S 10   b ). This integration method has been already described; hence, the description thereof will be omitted. The integration result is used as an identification likelihood per each track-coupling candidate/identification pair. 
     In step S 10   b , the identification likelihood calculation part  332   b  multiplies and normalizes identification likelihoods of track-coupling candidate/identification pairs, thus calculating an identification likelihood representing the entirety of a hypothesis. This calculation process of Embodiment 3 is identical to that of Embodiment 1. 
     Next, the identification likelihood calculation part  332  integrates a track-coupling likelihood per each track-coupling candidate/identification pair, calculated in step S 9 , and an identification likelihood of a hypothesis calculated in step S 10   b  (step S 11 ). This integration method of Embodiment 3 is identical to that of Embodiment 1. As described in Embodiment 1, it is possible to multiply track-coupling likelihoods of track-coupling candidate/identification pairs ascribed to a hypothesis in step S 9 , whereby the multiplication result can be integrated with identification likelihoods in step S 11 . 
     In step S 11 , the identification likelihood calculation part  332   b  stores combinations of hypotheses and integration results of likelihoods. 
     After step S 11 , the likelihood calculation part  33   b  repeatedly executes steps S 8  to S 11 . Upon determining nonexistence of hypotheses with uncalculated likelihoods in step S 8  (i.e. a decision result “No” of step S 8 ), the identification likelihood calculation part  332   b  stores a track/identification correlation likelihood, reflecting the current result of the likelihood calculation process (a loop consisting of steps S 8  to S 11 ) based on all the track-coupling candidate/identification pairs of hypotheses, in the likelihood storage  34  (step S 11   b ). The identification likelihood calculation part  332   b  stores an identification likelihood of a track-coupling candidate/identification pair in the likelihood storage  34  as an identification correlation probability in connection with a combination of an identification of the track-coupling candidate/identification pair and the current track of the track-coupling candidate. However, it is necessary to consider the situation in which the track-coupling candidate/identification pair possessing a combination of the current track and its identification is included in a plurality of hypotheses in common. Suppose the situation in which a hypothesis includes {(track  1 , track  4 ), ID 1 }; another hypothesis includes {(track  2 , track  4 ), ID 1 }; and a further hypothesis includes {(track  3 , track  4 ), ID 1 }, wherein track  4  indicates the current track. All the three track-coupling candidate/identification pairs include a combination “track  4 , ID 1 ” consisting of the current track and its identification. When a plurality of track-coupling candidate/identification pairs commonly possessing a combination of the current track and its identification exists, it is possible to add identification likelihoods, which are calculated with respect to a plurality of track-coupling candidate/identification pairs, so that the addition result is stored as a track/identification correlation likelihood regarding a combination of the current track and its identification. 
     Moreover, the identification likelihood calculation part  332   b  normalizes track/identification correlation likelihoods such that the sum of track/identification correlation likelihoods per each track becomes equal to “1”, so that the normalization result is stored in the likelihood storage  34 . With respect to two identifications ID 1 , ID 2 , for example, X represents an identification likelihood regarding a combination of track  4  and ID 1  while Y represents an identification likelihood regarding a combination of track  4  and ID 2 . Herein, X, Y are not directly regarded as track/identification correlation likelihoods, but X, Y are normalized such that the sum of a track/identification correlation likelihood regarding a combination of track  4  and ID 1  and a track/identification correlation likelihood regarding a combination of track  4  and ID 2  becomes equal to “1”. Herein, the identification likelihood calculation part  332   b  performs the following calculation using x, y representing normalized track/identification correlation likelihoods. 
     
       
         
           
             
               
                 
                   
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     That is, the identification likelihood calculation part  332   b  performs normalization by individually multiplying identification likelihoods X, Y, prior to normalization, by the reciprocal of its sum “1/(X+Y)”. 
     In step S 11   b , it is possible to solely perform a process of storing track/identification correlation likelihoods in the likelihood storage  34 . Alternatively, it is possible to perform a process of storing track/identification correlation likelihoods in the likelihood storage  34  while performing a process of deleting track/identification correlation likelihoods, irrelevant to the current track of a track-coupling candidate of a track-coupling candidate/identification pair, from track/identification correlation likelihoods stored in the likelihood storage  34 . 
     Track/identification correlation likelihoods stored in the likelihood storage  34  in step S 11   b  are used for execution of a loop consisting of steps S 8  to S 11 . 
     After step S 11   b , the identification likelihood calculation part  332   b  estimates a most-probable hypothesis with the highest integration value of likelihoods calculated in step S 11 , thus sending it to the identification result output device  4  (step S 12 ). The identification result output device  4  determines and presents which mobile body matches which track (step S 13 ). The steps S 12 , S 13  of Embodiment 3 are identical to those of Embodiment 1. 
     Embodiment 3 is able to demonstrate the same effect as Embodiment 1. Additionally, Embodiment 3 is designed to calculate likelihoods in consideration of not only the information detected in a predetermined time in the past from the current time but also the information detected in its previous time; hence, it is possible to estimate the correlation between tracks and identifications with a high precision.  FIG. 23  is a schematic illustration of the effect of Embodiment 3. Herein, a process for calculating likelihoods per each hypothesis at time u (i.e. a loop consisting of steps S 8  to S 11 ) is performed using data collected in a time range U which is a predetermined time in the past from time u. A track/identification correlation likelihood, which is determined by executing a loop consisting of steps S 8  to S 11  per each hypothesis, reflects data collected in the time range U (strictly speaking, its previous time). A process for calculating likelihoods per each hypothesis at time v is performed using data collected in a time range V which is a predetermined time in the past from time v. Additionally, the integration result of likelihoods in step S 11  is calculated incorporating the track/identification correlation likelihood calculated at time u. The integration result of likelihoods reflects not only the date of the time range V but also the data of its previous time range U. Since Embodiment 3 calculates likelihoods per each hypothesis by incorporating the previous information in the past, it is possible to determine the correlation between track and identifications with a higher precision. 
     Embodiment 4 
       FIG. 24  is a block diagram of a mobile body track identification system according to Embodiment 4 of the present invention. Herein, the same constituent elements as the constituent elements of Embodiment 1 are designated by the same reference numerals; hence, descriptions thereof will be omitted. The mobile body track identification system of Embodiment 4 includes the positional information detection device  1 , the identification detection information  2 , a mobile body track identification device  3 , an information history variation determination part  35 , a track-coupling candidate generation part  31   c , a hypothesis generation part  32   c , a likelihood calculation part  33   c , and the map storage  37 . 
     The information history variation determination part  35  retrieves combinations of track numbers, detection times, and positional coordinates from the positional information detection device  1  while retrieving combinations of detection times, identifications, and identification detection device numbers from the identification information detection device  2 . The information history variation determination part  35  holds positional coordinates, detection times, and track numbers, which are collected in a predetermined time in the past from the current time, from the positional information detection device  1 . Additionally, the information history variation determination part  35  holds the correlation between identifications, detection times, and identification detection device numbers, which are collected in a predetermined time in the past from the current time, from the identification information detection device  2 . At this time, they are held in a table form shown in  FIG. 10 . 
     As described above, the information history variation determination part  35  selectively holds data, ascribed to the predetermined time in the past, from among positional information and identifications retrieved from the positional information detection device  1  and the identification information detection device  2 , wherein it deletes the oldest positional information/identification upon receiving the positional information identification at the current time, thus updating its information held therein. Upon receiving the current positional information, its detection time, and its track number from the positional information detection device  1 , for example, it stores them while deleting the positional information at the oldest time. Similarly, upon receiving the current identification, its detection time, and its identification detection device number from the identification information detection device  2 , it additionally registers them in the table of  FIG. 10  while deleting the identification at the oldest time from the table. The updating process of the information history variation determination part  35  is identical to steps S 1 , S 2  of the track-coupling candidate generation part  31  and steps S 4 , S 5  of the hypothesis generation part  32 . 
     The information history variation determination part  35  sends positional information/identification held therein to the likelihood calculation part  33   c . That is, the likelihood calculation part  33   c  receives a combination of positional coordinates, its track number, and its detection time as well as the table content of  FIG. 10  (i.e. updated identifications). 
     Upon updating using newly retrieved positional information/identification from the positional information detection device  1  and the identification information detection device  2 , the information history variation determination part  35  makes a decision as to whether or not any variation occurs in the track number and the identification before and after updating. When no variation occurs in both the track number and the identification, it can be said that the same hypotheses may be repeatedly generated irrespective of reproduction of hypotheses. In this case, the information history variation determination part  35  instructs the likelihood calculation part  33   c  to estimate a most-probable hypothesis based on hypotheses which are used in previous calculation of likelihoods. In this case, no information is supplied to the track-coupling candidate generation part  31   c  and the hypothesis generation part  32   c.    
     In this connection, the same most-probable hypothesis as the previous one should be unnecessarily estimated using the same hypotheses as previous ones due to the occurrence of variations in positional coordinates after updating or due to a change of the identification detection device number correlated to identifications. 
     Additionally, hypotheses themselves are changed due to variations of track numbers or identifications. In an event of adding or discarding track numbers, for example, track-coupling candidates are changed so that generated hypotheses may be changed as well. This can be similarly applied to another event of adding or discarding identifications. Due to variations of track numbers or identifications, the information history variation determination part  35  of Embodiment 4 provides the track-coupling candidate generation part  31   c  with combinations of positional coordinates, detection times, and track numbers after updating while providing the hypothesis generation part  32   c  with the table content representing the correlation between identifications, detection times, and information detection device numbers. 
     The track-coupling candidate generation part  31   c  generates track-coupling candidates based on positional information and identifications supplied from the information history variation determination part  35 . To generate track-coupling candidates including positional coordinates of different tracks which are detected at the same time, the track-coupling candidate generation part  31   c  calculates positional coordinates of a median point as well. Subsequently, the track-coupling candidate generation part  31   c  sends track-coupling candidates to the hypothesis generation part  32   c . Upon calculating positional coordinates of a median point, the calculated positional coordinates are sent to the hypothesis generation part  32   c . The foregoing process of Embodiment 4 is identical to that of the track-coupling candidate generation part  31  of Embodiment 1. 
     The hypothesis generation part  32   c  generates and sends all the hypotheses to the likelihood calculation part  33   c  based on track-coupling candidates received from the track-coupling candidate generation part  31   c  and the updated table content received from the information history variation determination part  35 . 
     In this connection, the track-coupling candidate generation part  31   c  and the hypothesis generation part  32   c  of Embodiment 4 differ from the track-coupling candidate generation part  31  and the hypothesis generation part  32  in that they do not perform updating to hold positional information and identifications collected in a predetermined time in the past. 
     Upon receiving an instruction to estimate a most-probable hypothesis based on hypotheses which are used in previous calculation of likelihoods from the information history variation determination part  35 , the likelihood calculation part  33   c  calculates track-coupling likelihoods and identification likelihoods with respect to those hypotheses, thus calculating their integration value. The likelihood calculation part  33   c  estimates a hypothesis with the highest integration value of track-coupling likelihoods and identification likelihoods as a most-probable hypothesis, thus sending it to the identification result output device  4 . 
     Upon receiving hypotheses from the hypothesis generation part  32   c , the likelihood calculation part  33   c  calculates track-coupling likelihoods and identification likelihoods with respect to those hypotheses, thus calculating their integration value. The likelihood calculation part  33   c  estimates a hypothesis with the highest integration value of track-coupling likelihoods and integration likelihoods as a most-probable hypothesis, thus sending it to the identification result output device  4 . 
     The likelihood calculation part  33   c  receives combinations of detection times, positional coordinates, and track numbers as well as the updated table content (see  FIG. 10 ) from the information history variation determination part  35 . To calculate track-coupling likelihoods and identification likelihoods, the likelihood calculation part  33   c  utilizes the information updated by the information history variation determination part  35 . 
       FIG. 25  is a block diagram showing the constitution of the likelihood calculation part  33   c  of Embodiment 4. The likelihood calculation part  33   c  includes the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , an identification likelihood calculation part  332   c , and a hypothesis storage  333 . The hypothesis storage  333  is a memory that stores hypotheses among which the most-probable hypothesis has been already estimated. 
     The likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , and the identification likelihood calculation part  332   c  of Embodiment 4 perform the same processes as those of the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , and the identification likelihood calculation part  332  of Embodiment 1. When the information history variation determination part  35  issues an instruction to estimate a most-probable hypothesis among previous hypotheses, the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , and the identification likelihood calculation part  332   c  refer to hypotheses stored in the hypothesis storage  333  for use in their processing. On the other hand, upon receiving hypotheses from the hypothesis generation part  32   c , they refer to the received hypotheses for use in their processing. 
     Upon receiving an instruction to estimate a most-probable hypothesis based on previous hypotheses in which track-coupling candidates include different positional coordinates, which are detected at the same time but whose median point has not been calculated yet, the identification likelihood calculation part  332   c  calculates coordinates of a median point. 
     The information history variation determination part  35 , the track-coupling candidate generation part  31   c , the hypothesis generation part  32   c , and the likelihood calculation part  33   c  (including the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332   c , and the hypothesis storage  333 ) can be realized by way of a CPU of a computer which operates according to a mobile body track identification program. In this case, a program memory of a computer (not shown) stores a mobile body track identification program so that the CPU loads the program to thereby realize the functions of the information history variation determination part  35 , the track-coupling candidate generation part  31   c , the hypothesis generation part  32   c , and the likelihood calculation part  33   c . Alternatively, it is possible to configure the information history variation determination part  35 , the track-coupling candidate generation part  31   c , the hypothesis generation part  32   c , and the likelihood calculation part  33   c  by use of individual hardware elements. Similarly, it is possible to configure the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332   c , and the hypothesis storage  333  by use of individual hardware elements. In this connection, it is possible to arrange the hypothesis storage  333  outside the likelihood calculation part  33   c.    
     Next, the operation of the mobile body track identification device  3   c  of Embodiment 4 will be described. 
       FIGS. 26 and 27  are flowcharts showing the processing of the mobile body track identification device  3   c . Herein, the same steps as those of Embodiment 1 (see  FIGS. 4 and 5 ) are designated by the same reference numerals; hence, descriptions thereof will be omitted. 
     The information history variation determination part  35  retrieves combinations of track numbers, detection times, and positional information from the positional information detection device  1  (step S 41 ). The information history variation determination part  35  receives and additionally stores the current combination of positional information, its detection time, and its track number while deleting the oldest positional information, thus updating a positional information history (step S 42 ). The steps S 41 , S 42  of Embodiment 4 are identical to steps S 1 , S 2  of Embodiment 1. In Embodiment 4, however, the information history variation determination part  35  sends combinations of positional information, detection times, and track numbers, which are updated in step S 42 , to the likelihood calculation part  33   c.    
     The information history variation determination part  35  retrieves combinations of identification detecting times, identifications, and identification detection device numbers from the identification information detection device  2  (step S 43 ). Upon receiving the current combination of a detection time, an identification, and an identification detection device number, the information history variation determination part  35  additionally registers it with the table of  FIG. 10  while deleting the oldest identification (step S 44 ). The steps S 43 , S 44  of Embodiment 4 are identical to steps S 4 , S 5  of Embodiment 1. However, the information history variation determination part  35  sends the table content, which is updated in step S 44 , to the likelihood calculation part  33   c.    
     The information history variation determination part  35  makes a decision as to whether or not any track number is changed before and after updating in step S 42  and a decision as to whether or not any identification registered with the table is changed after updating in step S 44  (step S 45 ). Herein, variations of track numbers/identifications occur due to addition or discarding of track numbers/identifications. 
     In step S 45  when at least one of track numbers and identifications is changed (i.e. a decision result “Yes”), the information history variation determination part  35  sends updated combinations of positional information, detection times, and track numbers to the track-coupling candidate generation part  31   c  while sending the updated table content (representing the relationship between detection times, identifications, and identification detection device numbers) to the hypothesis generation part  32   c.    
     The track-coupling candidate generation part  31   c  generates all the track-coupling candidates based on combinations of positional information, detection times, and track numbers, thus sending them to the hypothesis generation part  32   c  (step S 3 ). With respect to track-coupling candidates including positional coordinates of different tracks which are detected at the same time, the track-coupling candidate generation part  31   c  calculates coordinates of a median point so as to send them to the likelihood calculation part  33   c.    
     The hypothesis generation part  32   c  generates all combinations of track-coupling candidate/identification pairs by use of identifications stored in the updated table received from the information history variation determination part  35  and track-coupling candidates received from the track-coupling candidate generation part  31   c  in step S 3  (step S 6 ). Next, the hypothesis generation part  32   c  generates all the hypotheses satisfying the first to third conditions described in Embodiment 1 (step S 7 ). The hypothesis generation part  32   c  sends hypotheses, generated in step S 7 , to the likelihood calculation part  33   c . The steps S 3 , S 6 , and S 7  of Embodiment 4 are identical to those of Embodiment 1. 
     When the hypothesis generation part  32   c  generates hypotheses, the likelihood calculation part  33   c  retrieves those hypotheses for use in its processing so as to perform a series of steps S 8  to S 12  identical to those of Embodiment 1. Specifically, the likelihood calculation control part  330  makes a decision as to whether or not any hypothesis with uncalculated likelihoods exists in hypotheses received in step S 7  (step S 8 ). When hypotheses with uncalculated likelihoods exist (i.e. a decision result “Yes” of step S 8 ), the likelihood calculation control part  330  selectively sends one of hypotheses with uncalculated likelihoods to the track-coupling likelihood calculation part  331  and the identification likelihood calculation part  332   c.    
     The track-coupling likelihood calculation part  331  calculates a tack-coupling likelihood per each track-coupling candidate/identification pair ascribed to the received hypothesis (step S 9 ). Herein, positional coordinates and its detection time used for track-coupling likelihood calculation has been provided by the information history variation determination part  35  as an updated combination of positional coordinates, its detection time, and its track number. 
     The identification likelihood calculation part  332   c  calculates an identification likelihood with respect to the received hypothesis (step S 10 ). Herein, positional coordinates and its detection time has been provided by the information history variation determination part  35 . Additionally, the updated table content representing the correlation between an identification, its detection time, and its identification detection device number has been provided by the information history variation determination part  35 . Therefore, the identification likelihood calculation part  332   c  of Embodiment 4 uses the positional information and identification to calculate an identification likelihood of a hypothesis as similar to Embodiment 1. 
     Next, the identification likelihood calculation part  332   c  integrates a track-coupling likelihood of a hypothesis, calculated in step S 9 , and an identification likelihood of the hypothesis, calculated in step S 10 , so as to store the correlation between the integration result and the hypothesis (step S 11 ). After step S 11 , a loop consisting of steps S 8  to S 11  is repeated. 
     When hypotheses with uncalculated likelihoods disappear, the identification likelihood calculation part  332   c  estimates a hypothesis with the highest integration value of a track-coupling likelihood and an identification likelihood, calculated in step S 11 , as a most-probable hypothesis, thus sending it to the identification result output device  4  (step S 12 ). Herein, the identification likelihood calculation part  332   c  of Embodiment 4 stores all the hypotheses, correlated to integration results of track-coupling likelihoods and identification likelihoods, (i.e. hypotheses generated by the hypothesis generation part  32   c ) in the hypothesis storage  333 , thus updating the stored content. 
     When no change occurs in any of track numbers and identifications in step S 45  (i.e. a decision result “No”), the information history variation determination part  35  instructs the likelihood calculation part  33   c  to estimate a most-probable hypothesis based on hypotheses used for previous likelihood calculation (i.e. hypotheses stored in the hypothesis storage  333 ). In response to this instruction, the likelihood calculation control part  330  reads hypotheses stored in the hypothesis storage  333  (step S 46 ). The likelihood calculation part  33   c  executes steps S 8  to S 12  on those hypotheses similar to Embodiment 1. First, the likelihood calculation control part  330  makes a decision as to whether or not any hypothesis with uncalculated likelihoods exists in the read hypotheses from the hypothesis storage  333  (step S 8 ). When hypotheses with uncalculated likelihoods exist (i.e. a decision result “Yes” of step S 8 ), the likelihood calculation control part  330  selectively sends one of those hypotheses with uncalculated likelihoods to the track-coupling likelihood calculation part  331  and the identification likelihood calculation part  332   c.    
     The track-coupling likelihood calculation part  331  calculates a track-coupling likelihood per each track-coupling candidate/identification pair ascribed to the received hypothesis (step S 9 ). Herein, positional coordinates and its detection time, used for calculating a track-coupling likelihood, has been provided by the information history variation determination part  35  as an updated combination of positional coordinates, its detection time, and its track number. 
     The identification likelihood calculation part  332   c  calculates an identification likelihood of the received hypothesis (step S 10 ). As described above, the information history variation determination part  35  has provided an updated identification so that it is possible to calculate an identification likelihood of the hypothesis similar to Embodiment 1. When the newly registered detection time refers to different positional coordinates which are detected at the same time, however, coordinates of a median point has not be calculated yet; hence, the identification likelihood calculation part  332   c  calculates positional coordinates of a median point so as to provide them for used in calculation of an identification likelihood. 
     Next, the identification likelihood calculation part  332   c  integrates the track-coupling likelihood, calculated in step S 9 , and the identification likelihood, calculated in step S 10 , thus storing the integration result in connection with its hypothesis (step S 11 ). After step S 11 , a loop consisting of steps S 8  to S 11  is repeated. When hypotheses with uncalculated likelihoods disappear, the identification likelihood calculation part  332   c  estimates a hypothesis with the highest integration value of the track-coupling likelihood and the identification likelihood, calculated in step S 11 , as a most-probable hypothesis, thus sending it to the identification result output device  4  (step S 12 ). These steps S 11 , S 12  are identical to the foregoing steps in which the hypothesis generation part  32   c  generates new hypotheses. When hypotheses stored in the hypothesis storage  33  are used for processing, the identification likelihood calculation part  332   c  does not update the stored content of the hypothesis storage  333 . 
     The identification result output device  4  determines and presents which mobile body corresponds to which track based on track-coupling candidate/identification pairs ascribed to the most-probable hypothesis estimated in step S 12  (step S 13 ). At this time, the identification result output device  4  may specifically display the position or track of a mobile body. The step S 13  of Embodiment 4 is identical to that of Embodiment 1. 
     Embodiment 4 estimates a most-probable hypothesis based on hypotheses used for previous calculation of likelihoods when no changes occur in track numbers and identifications irrespective of variations of positional coordinates and variations of detection times of positional coordinates/identifications. When no changes occur in track numbers and identifications, the same track-coupling candidates and hypotheses as those provided by previous calculation are calculated. This eliminates a repetitive process for repetitively generating the same hypotheses by the hypothesis generation part  32   c , thus reducing the amount of calculation in determining the correlation between tracks and identifications. 
     In Embodiment 4, it is possible for the mobile body track identification device  3   c  to start step S 41  and its following steps upon a trigger of an external instruction without using a trigger input from the positional information detection device  1  or the identification information detection device  2 . In this case, information is not updated in steps S 42 , S 44  without a new input from the positional information detection device  1  and the identification information detection device  2  after previous estimation of a most-probable hypothesis. For this reason, no changes occur in track numbers and identifications (i.e. a decision result “No” of step S 45 ). This allows the flow to proceed to step S 46 , whereas if no input comes from the positional information detection device  1  and the identification information detection device  2  after previous estimation of a most-probable hypothesis, the same most-probable hypothesis will be estimated. Therefore, when the mobile body track identification device  3   c  receives a processing start instruction without an input from the positional information detection device  1  and the identification information detection device  2  after previous estimation of a most-probable hypothesis, the mobile body track identification device  3   c  may directly send the previously estimated most-probable hypothesis to the identification result output device  4 . 
     Similar to the foregoing variation of Embodiment 1, it is possible for the likelihood calculation parts  33   b  and  33   c  of Embodiments 3 and 4 not to adopt the track-coupling likelihood calculation part  331 , thus precluding calculation of track-coupling likelihoods. In this case, the identification likelihood calculation parts  332   b ,  332   c  do not necessarily integrate track-coupling likelihoods and identification likelihoods, thus estimating hypotheses with maximum identification likelihoods as most-probable hypotheses. 
     In Embodiment 4, when the likelihood calculation part  33   c  precludes the track-coupling likelihood calculation part  331  so as not to calculate track-coupling likelihoods, the information history variation determination part  35  makes a decision as to whether or not any change occurs in identifications in step S 45 . The flow proceeds to step S 3  when any change occurs (a decision result “Yes” of step S 45 ), whilst the flow proceeds to step S 46  when any change does not occur (a decision result “No” of step S 45 ). 
     It is possible for the likelihood calculation parts  33   b ,  33   c  of Embodiments 3, 4 to preclude the track-coupling likelihood calculation part  331 , wherein it is possible for the mobile body track identification devices  3   b ,  3   c  to include the track-coupling likelihood calculation part  331   a  similar to Embodiment 2. In this case, the identification likelihood calculation parts  332   b ,  332   c  estimate hypotheses with the highest identification likelihoods as most-probable hypotheses. This constitution demonstrates the same effect as Embodiment 2. Moreover, it is possible to combine Embodiments 3 and 4 together. 
     Embodiment 5 
       FIG. 28  is a block diagram of a mobile body track identification system according to Embodiment 5 of the present invention. Herein, the same constituent elements as the constituent elements of Embodiment 1 are designated by the same reference numerals; hence, descriptions thereof will be omitted. The mobile body track identification system of Embodiment 5 includes the positional information detection device  1 , the identification information detection device  2 , a mobile body track identification device  3   d , the identification result output device  4 , and an attribute detection device  5 . The mobile body track identification device  3   d  includes a track-coupling candidate generation part  31 , a hypothesis generation part  32   d , a likelihood calculation part  33   d , and the map storage  37 . 
     The mobile body track identification system of Embodiment 5 does not necessarily include a single attribute detection device  5 ; hence, it is possible to include a plurality of attribute detection devices  5 . Herein, an attribute detection device ID is allocated to the attribute detection device  5  in order to univocally specify it in advance. Embodiment 5 adopts a number as an attribute detection device ID, which will be referred to as an attribute detection device number. 
     The attribute detection device  5  detects an attribute of a mobile body in the tracking area  50  (see  FIG. 1 ). The attribute represents an attribute or a feature of a mobile body, for example, wherein it is possible to use the color, size, weight, and temperature of a mobile body. With respect to a mobile body representing a person, it is possible to use the sex and age of the person as his/her attribute. The attribute may represents an attribute of a mobile body such as information (e.g. color, shape, etc.) which can be discriminated from an image produced by shooting a mobile body. Alternatively, it is possible to use information (e.g. weight, etc.) which can be detected using a sensor or a measuring instrument without shooting an image of a mobile body. 
     The attribute detection device  5  can be realized using a device which is able to detect an attribute of a mobile body in the tracking area  50  so as to specify its detection time and positional coordinates. For instance, it is possible to realize the attribute detection device  5  by use of a camera, a pressure sensor, a temperature sensor, and a thermography. 
     A plurality of attribute detection devices  5  can be arranged in such a way that their detection areas overlap with each other. Alternatively, they can be arranged in such a way that their detection areas do not overlap with each other. Moreover, it is possible to simultaneously use a plurality of attribute detection devices  5  ascribed to different types. 
     The attribute detection device  5  may not always detect an attribute of a mobile body which actually exists; hence, a decision as to whether or not an attribute is detected depends on the installed condition of the attribute detection device  5 . When the attribute detection device  5  is arranged in the tracking area  50 , for example, the attribute detection device  5  may easily detect the attribute of a mobile body which exists close to it, whilst the attribute detection device  5  may hardly detect the attribute of a mobile body which is distanced from it. 
     The attribute detection device  5  sends an attribute of a detected mobile body, an attribute type univocally representing the type of an attribute, an attribute detection time, positional coordinates, and an attribute detection device number to the mobile body track identification device  3   d . When the attribute detection device  5  operates to detect an attribute of a mobile body but fails to do so, it may send a detection time and an attribute detection device number to the mobile body track identification device  3   d  together with an attribute, an attribute type, and positional coordinates, all of which are described as “null”. Alternatively, the attribute detection device  5  may not necessarily send any attribute to the mobile body track identification device  3   d , thus allowing the mobile body track identification device  3   d  to determine that no attribute is detected at that time. 
     When the attribute detection device  5  has a narrow detection area so that positional coordinates of the installed position thereof are deemed equivalent to positional coordinates of a mobile body at the time of detecting its attribute, the attribute detection device  5  sends positional coordinates of the installed position thereof to the mobile body track identification device  3   d  as positional coordinates of a mobile body. A pressure sensor may exemplify the attribute detection device  5 . In this case, the attribute detection device  5  needs to hold positional coordinates of its own installed position in the tracking area  50  in advance. 
     When the attribute detection device  5  discriminates an attribute from an image which is shot using a camera or the like so that the scope of a shot image is broad, the installed position thereof does not necessarily match with the position of a mobile body at the time of shooting its image and extracting its attribute. In this case, the attribute detection device  5  stores a conversion table for converting an arbitrary position of a shot image into positional coordinates of the tracking area  50  in advance, whereby the position of a mobile body in the shot image is converted into positional coordinates in the tracking area  50  with reference to the conversion table. It is possible to preset a conversion equation, instead of the conversion table, for converting arbitrary positional coordinates in a shot image into positional coordinates in the tracking area  50 , whereby the position of a mobile body in the shot image is put into the conversion equation and converted into positional coordinates in the tracking area  50 . 
     As described above, positional coordinates of a mobile body at the time of detecting its attribute can be calculated by way of a method suited to the type of the attribute detection device  5  (i.e. the attribute type). 
     It is necessary to establish synchronization between positional information of the positional information detection device  1 , an identification of the identification information detection device  2 , and an attribute of the attribute detection device  5 . In other words, the positional information detection device  1  detects positional information of a mobile body; the identification information detection device  2  detects its identification; and the attribute detection device  5  detects its attribute at the same time. When the positional information detection device  1 , the identification information detection device  2 , and the attribute detection device  5  detect positional information, its identification, and its attribute in an asynchronous manner, the mobile body track identification device  3   d  may save the positional information, identification, and attribute for a predetermined time in a buffer, so that the positional information, identification, and attribute accumulated in the buffer are read out every predetermined time. Alternatively, when no timing synchronization is established between the positional information detection device  1 , the identification information detection device  2 , and the attribute detection device  5 , it is possible to apply the same detection time to the positional information, its identification, and its attribute, which the mobile body track identification device  3   d  receives simultaneously. 
     Similar to Embodiment 1, the hypothesis generation part  32   d  of Embodiment 5 receives track-coupling candidates from the track-coupling candidate generation part  31  while receiving identifications from the identification information detection device  2 . Additionally, the hypothesis generation part  32   d  receives attributes, attribute types, detection times, positional coordinates, and attribute detection device numbers from the attribute detection device  5 , thus holding their correlations. 
     The hypothesis generation part  32   d  generates all the hypotheses based on track-coupling candidates of the track-coupling candidate generation part  31 , identifications of the identification information detection device  2 , and attributes of the attribute detection device  5 , thus sending those hypotheses to the likelihood calculation part  33   d.    
     Next, a hypothesis generating procedure of Embodiment 5 will be described in detail. First, the hypothesis generation part  32   d  generates combinations of track-coupling candidates and attributes by use of track-coupling candidates received from the track-coupling candidate generation part  31 , and attributes which the attribute detection device  5  detects in a time range from a predetermined time in the past to the current time. Hereinafter, those combinations will be referred to as attribute-attached track-coupling candidates. 
     The hypothesis generation part  32   d  selects track-coupling candidates, correlated to attributes, in accordance with the following procedure, thus generating attribute-attached track-coupling candidates. The hypothesis generation part  32   d  selects one of attributes received from the attribute detection device  5  so as to calculate a euclidean distance between positional coordinates regarding the selected attribute and positional coordinates of a track-coupling candidate at its detection time with respect to all the combinations of track-coupling candidates combined with the selected attribute. The hypothesis generation part  32   d  specify a combination with the shortest euclidean distance among combinations of track-coupling candidates and attributes so as to store the correlation between the track-coupling candidate and the attribute ascribed to the specified combination. This process is performed once with respect to all the received attributes. When a track-coupling candidate includes interruption between tracks so that positional coordinates at the detection time of the selected attribute cannot be directly obtained from the track-coupling candidate, the hypothesis generation part  32   d  performs interpolation between interrupted tracks, based on positional coordinates of disappearance and its disappearance time and positional coordinates of appearance and its appearance time which are included in the track-coupling candidate, so as to calculate positional coordinates at the attribute detection time, thus calculating a euclidean distance with positional coordinates of the attribute. When a track-coupling candidate includes a plurality of tracks which are detected at the attribute detection time and a plurality of positional coordinates of a mobile body at the attribute detection time, it is necessary to calculate a euclidean distance between a median point and positional coordinates of the attribute. 
     The result of the foregoing process may allow for existence of any track-coupling candidate correlated with no attribute. Of course, it may allow for existence of any track-coupling candidate correlated with a plurality of attributes. Suppose an example in which the track-coupling candidate generation part  31  generates track-coupling candidates T 1 , T 2 , . . . , Tn while the attribute detection device  5  detects attributes A 1 , A 2 , . . . , Am. Herein, nonexistence of any attribute correlated to each track-coupling candidate can be represented using a character string “no attribute”. In this case, for example, the hypothesis generation part  32   d  generates combinations of (T 1 , A 1 ), (T 2 , A 2 ), . . . , (Tn, Am) or combinations of (T 1 , no attribute), (T 2 , T 1 ), (T 3 , A 2 , A 3 ), . . . , (Tn, Am- 2 , Am- 1 , Am), which are stored as attribute-attached track-coupling candidates. 
     The hypothesis generation part  32   d  may generate attribute-attached track-coupling candidates such that in such a way that all the received attributes are each certainly correlated to any one of track-coupling candidates. With respect to a track-coupling candidate having the shortest euclidean distance, with positional coordinates regarding a certain attribute, which exceeds a predetermined threshold, the hypothesis generation part  32   d  may determine that none of track-coupling candidates is correlated to the attribute. 
     The hypothesis generation part  32   d  generates all the combinations of attribute-attached track-coupling candidates and identifications by use of attribute-attached track-coupling candidates, which are produced according to the foregoing procedure, and identifications which are detected by the identification information detection device  2  in a time range from a predetermined time in the past to the current time. The hypothesis generation part  32   d  may focus on attribute-attached track-coupling candidates so as to recognize them as simple track-coupling candidates, so that the hypothesis generation part  32   d  generates all the combinations of attribute-attached track-coupling candidates and identifications in accordance with the foregoing method of Embodiment 1 for generating track-coupling candidate/identification pairs. Hereinafter, they will be referred to as attribute-attached track-coupling candidate/identification pairs. 
     Similar to Embodiment 1, the hypothesis generation part  32   d  of Embodiment 5 extracts all the sets of attribute-attached track-coupling candidate/identification pairs, thus determining individual sets as hypotheses. 
     The likelihood calculation part  33   d  calculates and integrates a track-coupling likelihood, an identification likelihood, and an attribute likelihood per each of hypotheses generated by the hypothesis generation part  32   d . Subsequently, it estimates a hypothesis with the highest integration value of the track-coupling likelihood, the identification likelihood, and the attribute likelihood as a most-probable hypothesis, thus sending it to the identification result output device  4 . 
     The attribute likelihood is likelihood in which individual attributes, correlated to track-coupling candidates included in attribute-attached track-coupling candidates ascribed to each hypothesis, represent the same mobile body. When an attribute correlated to a track-coupling candidate represents the color of clothes, for example, the attribute likelihood may represent a similarity of color of clothes. Alternatively, the attribute likelihood may represent elements other than colors of clothes, such as a similarity of heights, a similarity of weights, a similarity of ages, and a similarity of sexes, in response to attribute types. 
       FIG. 29  is a block diagram showing the constitution of the likelihood calculation part  33   d  of Embodiment 5. The likelihood calculation part  33   d  includes a likelihood calculation control part  330   d , the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332 , and an attribute likelihood calculation part  334 . Herein, the track-coupling likelihood calculation part  331  and the identification likelihood calculation part  332  are identical to Embodiment 1; hence, descriptions thereof will be omitted. 
     The likelihood calculation part  330   d  sends hypotheses with uncalculated likelihoods to the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332 , and the attribute likelihood calculation part  334 . 
     The attribute likelihood calculation part  334  receives hypotheses generated by the hypothesis generation part  32   d , thus calculating attribute likelihoods with respect to hypotheses. To individually calculate attribute likelihoods of hypotheses, the attribute likelihood calculation part  334  calculates attribute likelihoods of track-coupling candidates ascribed to each hypothesis and then multiplies and normalizes attributes likelihoods of track-coupling candidates ascribed to each hypothesis, thus determining an attribute likelihood of each hypothesis. 
     The attribute likelihood calculation part  334  has determined calculation methods of attribute likelihoods in connection with attribute types, thus determining an appropriate calculation method in response to the attribute type included in each hypothesis. Thus, the attribute likelihood calculation part  334  calculates attribute likelihoods in accordance with a calculation method corresponding to each attribute type. 
     Additionally, attribute-attached track-coupling candidates included in each hypothesis may include track-coupling candidates ascribed to one attribute type or track-coupling candidates ascribed to plural attribute types. 
     Next, the operation of the attribute likelihood calculation part  334  for calculating attribute likelihoods with respect to attribute-attached track-coupling candidates will be described. For the sake of simplification of description, the following description refers to a calculation process for calculating track-coupling candidates each correlated to two attributes ascribed to the same attribute type. 
     Additionally, attributes are represented using histograms, wherein the number of bins (Bin) applied to each histogram is set to “m”. Herein, two attributes (histograms) are denoted by h 1 , h 2 , wherein the frequencies of bins i of histograms are denoted by h 1 ( i ), h 2 ( i ). It is possible to name the color of clothes as the attribute type representing these histograms. The color of clothes can be expressed using histograms in the color space. Specifically, the attribute detection device  5  generates histograms each counting the number of pixels, included in an image of shooting a mobile body, per each color type, thus setting those histograms as attributes. When histograms are used to represent attributes, the attribute likelihood calculation part  334  calculates a Bhattacharyya distance D according to Equation 4, thus determining an attribute likelihood such that a shorter Bhattacharyya distance D indicates a higher attribute likelihood whilst a longer Bhattacharyya distance D indicates a lower attribute likelihood. 
     
       
         
           
             
               
                 
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     The above example is designed to determine attribute likelihoods, represented by histograms, based on Bhattacharyya distances; but it is possible to calculate attribute likelihoods in accordance with other methods. 
     A consideration is made with respect to the case where mobile bodies are classified using attribute types representing sexes so that numerical values or character strings corresponding to classifications are set to attributes. For instance, an attribute “0” is set to a mobile body representing a male whilst an attribute “1” is set to a mobile body representing a female. Alternatively, it is possible to use character strings such as “male” and “female” instead of numerical values “0” and “1”. The attribute likelihood calculation part  334  holds definitions of attribute likelihoods suited to combinations of attributes correlated to track-coupling candidates with respect to attribute types. At this time, attributes are defined such that a higher attribute likelihood is calculated with respect to a combination of the same attributes whilst a lower attribute likelihood is calculated with respect to a combination of different attributes. When an attribute correlated to a track-coupling candidate is “male/male” or “female/female”, for example, the attribute may have a high likelihood of indicating the same mobile body; hence, it is defined with a high attribute likelihood. On the other hand, an attribute “male/female” correlated to a track-coupling candidate is defined with a low attribute likelihood. The attribute likelihood calculation part  334  holds those definitions of attributes, whereby it is possible to read attribute likelihoods in response to attribute patterns correlated to track-coupling candidates. 
     A consideration is made with respect to the case where attribute likelihoods are expressed as numerical values representing features of mobile bodies. As this attribute type, it is possible to name height, weight, age, or the like. In this case, the attribute likelihood calculation part  334  calculates a variance between two values correlated to an attribute likelihood of a track-coupling candidate. The attribute likelihood calculation part  334  holds a function (hereinafter, referred to as a “likelihood function”), in which a smaller variance indicates a higher attribute likelihood whilst a larger variance indicates a lower attribute likelihood, in advance. The attribute likelihood calculation part  334  applies a calculated variance to the likelihood function so as to calculate an attribute likelihood. Herein, attribute likelihoods are calculated using variances, but it is possible to calculate attribute likelihoods in accordance with other methods. 
     The above description refers to the case where two attributes of the same attribute type are correlated to a track-coupling candidate. In the case where three or more attributes of the same attribute type are correlated to a track-coupling candidate, it is possible to calculate attribute likelihoods of track-coupling candidates in accordance with the following procedure. 
     First, the attribute likelihood calculation part  334  sequences attributes correlated to track-coupling candidates in an order of earlier detection times. Next, the attribute likelihood calculation part  334  calculates an attribute likelihood between adjacent attributes which adjoin together in their places of order. For instance, a likelihood is calculated between a first attribute and a second attribute; similarly, a likelihood is calculated between second and third attributes; subsequently, a likelihood is calculated between third and fourth attributes. Thereafter, attribute likelihoods are sequentially calculated. At this time, a likelihood between two attributes which adjoin together in their places of order is calculated by the foregoing calculation method applied to a track-coupling candidate correlated to two attributes. 
     The attribute likelihood calculation part  334  calculates a likelihood per each pair of attributes which adjoin together in their places of order; thereafter, the attribute likelihood calculation part  334  multiplies attribute likelihoods and then calculates a radical root of the number of attribute likelihoods subjected to multiplication. It implements normalization on attributes related to attribute-attached track-coupling candidates by way of the radical root calculation. 
     In the above process of multiplying attribute likelihoods, it is possible to perform weighted calculation on attribute likelihoods in response to the attribute detection device number corresponding to an original attribute and then multiply them. This is a measure considering the property of the attribute detection device  5 , wherein the attribute detection device  5  that is able to detect the front portion of a face of a mobile body (herein, a person) is able to detect its attribute such as sex and age with a high precision. On the other hand, the attribute detection device  5  that is able to detect a person&#39;s face solely in a slanted direction may include an error in a detected attribute. As described above, attribute likelihoods are subjected to weighting, reflecting a detection precision per each attribute detection device, and then multiplied together. It is unnecessary to receive the attribute detection device number when weighting is not necessarily performed in response to the attribute detection device  5 . 
     Next, a calculation method of attribute likelihoods will be described with respect to track-coupling candidates each correlated to a plurality of attributes of different attribute types. In this case, the attribute likelihood calculation part  334  calculates attribute likelihoods regarding track-coupling candidates in accordance with the following procedure. 
     First, the attribute likelihood calculation part  334  sequences attributes correlated to track-coupling candidates in an order of earlier detection times. Subsequently, the attribute likelihood calculation part  334  classifies attributes and then calculates attribute likelihoods per each attribute type. To calculate attribute likelihoods with regard to a single attribute type, the attribute likelihood calculation part  334  generates combinations of attributes, which adjoin together in their places of order, with respect to the attribute type which is currently focused on, thus calculating a attribute likelihood per each combination. Herein, attributes which adjoin together in their places of order are attributes which adjoin together in their places of order with respect to the attribute type, which is currently focused on, and between which another attribute of the same attribute type is interposed. In this connection, another attribute of another attribute type may exist between attributes of the same attribute type. Additionally, an attribute likelihood per each combination of attributes of the same attribute type which adjoin together in their places of order can be calculated in a manner similar to the foregoing operation of calculating a likelihood regarding a track-coupling candidate correlated to two attributes. The attribute likelihood calculation part  334  calculates an attribute likelihood per each combination of attributes of the same type which adjoin together in their places of order; thereafter, it multiplies attribute likelihoods and then perform normalization by calculating a radical root of the number of attribute likelihoods subjected to multiplication. As described above, it is possible to calculate attribute likelihoods with regard to the attribute type which is currently focused on. Similarly, the attribute likelihood calculation part  334  calculates attribute likelihoods with regard to another attribute type. 
     As described above, after calculating attribute likelihoods per each attribute type, the attribute likelihood calculation part  334  multiplies attribute likelihoods and then performs normalization by calculating a radical root of the number of attribute likelihoods subjected to multiplication. Thus, it is possible to calculate attribute likelihoods with respect to attribute-attached track-coupling candidates in which each track-coupling candidate is correlated to a plurality of attributes of different attribute types. To multiply attribute likelihoods which are calculated per each attribute type, it is possible to multiply attribute likelihoods by weight coefficients in response to their types and then multiply them together. It is possible to reflect superiority/inferiority of reliability per each attribute type in attribute likelihoods by multiplying attribute likelihoods by weight coefficients. In the case where a person&#39;s sex can be detected with a high precision whilst an error may occur in detecting the color of clothes, for example, an attribute likelihood calculated using a person&#39;s sex needs to be multiplied by a high weight coefficient while an attribute likelihood calculated using a color needs to be multiplied by a small weight coefficient. 
     Next, an example of calculation of attribute likelihoods will be described with reference to the case where track-coupling candidates are each correlated to a plurality of attributes of different attribute types. Herein, track-coupling candidates are each correlated to five attributes. Among attributes sequences in an order of earlier detection times, first, third, and fourth attributes are ascribed to an attribute type of “color” whilst second and fifth attributes are ascribed to an attribute type of “sex”. In this case, the attribute likelihood calculation part  334  calculates attribute likelihoods with respect to a pair of first and third attributes and a pair of third and fourth attributes. This produces two attributes likelihoods, wherein the attribute likelihood calculation part  334  multiplies them together and then calculates a square root. This produces an attribute likelihood with respect to the attribute type of “color”. Next, an attribute likelihood regarding the attribute type of “sex” is calculated based on second and fifth attributes. Thereafter, the attribute likelihood calculation part  334  multiplies the attribute likelihood regarding “color” and the attribute likelihood regarding “sex” and then calculates a square root. Thus, it is possible to calculate an attribute likelihood per each track-coupling candidate correlated to five attributes. 
     Additionally, when track-coupling candidates are not correlated to attributes, or when track-coupling candidates are each correlated to a single attribute, the attribute likelihood calculation part  334  sets a constant (e.g. “1”) to attribute likelihoods of those track-coupling candidates. 
     As described above, the attribute likelihood calculation part  334  multiplies and normalizes attribute likelihoods calculated for track-coupling candidates ascribed to the same hypothesis, thus calculating an attribute likelihood of the hypothesis. This normalization is realized by calculating a radical root of the number of track-coupling candidates ascribed to the same hypothesis. 
     The attribute likelihood calculation part  334  integrates a track-coupling candidate, an identification likelihood, and an attribute likelihood per each hypothesis. This integration process is implemented by multiplying the track-coupling likelihood, the identification likelihood, and the attribute likelihood. In the integration process, the attribute likelihood calculation part  334  may apply weights to the track-coupling likelihood, the identification likelihood, and the attribute likelihood so as to multiply them together. 
     For instance, the track-coupling candidate generation part  31 , the hypothesis generation part  32   d , and the likelihood calculation part  33   d  (including the likelihood calculation control part  330   d , the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332 , and the attribute likelihood calculation part  334 ) can be realized by way of a CPU of a computer that operates according to a mobile body track identification program. In this case, a program memory of a computer (not shown) stores a mobile body track identification program so that the CPU loads the program so as to realize the functions of the track-coupling candidate generation part  31 , the hypothesis generation part  32   d , and the likelihood calculation part  33   d . Alternatively, it is possible to realize the track-coupling candidate generation part  31 , the hypothesis generation part  32   d , and the likelihood calculation part  33   d  by use of individual hardware elements. Similarly, it is possible to realize the likelihood calculation control part  330   d , the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332 , and the attribute likelihood calculation part  334  by use of individual hardware elements. 
     Next, the operation of the mobile body track identification device  3   d  of Embodiment 5 will be described.  FIGS. 30 and 31  are flowcharts showing the processing of the mobile body track identification device  3   d  of Embodiment 5. Herein, the same steps as the steps of Embodiment 1 (see  FIGS. 4 and 5 ) are designated by the same reference numerals; hence, descriptions thereof will be omitted. In  FIG. 30 , a series of steps (i.e. steps S 1  to S 5 ) for updating the table holding retrieved identifications is identical to that of Embodiment 1 (see  FIG. 4 ). 
     The hypothesis generation part  32   d  retrieves combinations of detection times, attributes, attribute types, positional coordinates, and attribute detection device numbers from the attribute detection device  5  (step S 51 ). Upon detecting an attribute of a mobile body, the attribute detection device  5  sends it to the hypothesis generation part  32   d . The hypothesis generation part  32   d  holds the correlation between detection times, attributes, attribute types, positional coordinates, and attribute detection device numbers in a table. 
     The hypothesis generation part  32   d  holds attributes which have been received from the attribute detection device  5  in a predetermined time in the past from the current time (i.e. the time of retrieving the current attribute). With respect to the current attribute, the hypothesis generation part  32   d  retrieves a combination of the attribute, its detection time, its attribute type, its positional coordinates and its attribute detection device number so as to additionally register it with the table while deleting the oldest attribute (step S 52 ). For instance, the hypothesis generation part  32   d  has held the correlation between attributes, detection times, attribute types, positional coordinates, and attribute detection device numbers at times t 1  to tn- 1 . Thereafter, upon receiving a combination of an attribute, its detection time, its attribute type, its positional coordinates, and its attribute detection device number at the current time tn, the hypothesis generation part  32   d  registers the attribute received at time tn with the table while discarding the attribute received at time t 1  from the table. 
     In this connection, the hypothesis generation part  32   d  does not only store the updated table by itself but also sends it to the likelihood calculation part  33   d.    
     The hypothesis generation part  32   d  generates combinations of track-coupling candidates and attributes (i.e. attribute-attached track-coupling candidates) by use of attributes stored in the table updated in step S 52  and track-coupling candidates received from the track-coupling candidate generation part  31  in step S 3  (step S 53 ). Specifically, the hypothesis generation part  32   d  selects one of received attributes so as to calculate an euclidean distance between positional coordinates of the selected attribute and positional coordinates of a track-coupling candidate at its detection time with respect to each of combinations of the selected attribute and all the combinations of track-coupling candidates. The hypothesis generation part  32   d  specifies a combination of the attribute and a track-coupling candidate with the shortest euclidean distance so as to store it as an attribute-attached track-coupling candidate. The hypothesis generation part  32   d  performs this process once with respect to all the attributes retrieved in step S 51 . With respect to a track-coupling candidate with interruption of tracks, the hypothesis generation part  32   d  performs interpolation between interrupted tracks so as to specify positional coordinates of the track-coupling candidate, thus calculating an euclidean distance with positional coordinates of an attribute. When a track-coupling candidate whose attribute includes a plurality of tracks at its detection time so that a plurality of positional coordinates is detected, an euclidean distance is calculated between their median point and positional coordinates of each attribute. 
     In step S 53 , a single track-coupling candidate is not necessarily correlated to a single attribute; hence, a single track-coupling candidate may be correlated to two or more attributes. Alternatively, there may exist track-coupling candidates which are not at all correlated to any attribute. 
     The hypothesis generation part  32   d  generates all the combinations of identifications and attribute-attached track-coupling candidates (hereinafter, referred to as “attribute-attached track-coupling candidate/identification pairs”) by use of identifications stored in the table updated in step S 5  and attribute-attached track-coupling candidates generated in step S 53  (step S 6   d ). Herein, the hypothesis generation part  32   d  focuses solely on track-coupling candidates in attribute-attached track-coupling candidates so as to generate all the combinations of attribute-attached track-coupling candidate/identification pairs by way of a method similar to step S 6  of Embodiment 1 (see  FIG. 4 ). 
     Subsequently, the hypothesis generation part  32   d  generates hypotheses as sets of attribute-attached track-coupling candidate/identification pairs (step S 7   d ). In step S 7   d , the hypothesis generation part  32   d  focuses on track-coupling candidates and identifications in attribute-attached track-coupling candidate/identification pairs so as to extract all the sets of attribute-attached track-coupling candidate/identification pairs satisfying the first to third conditions similar to Embodiment 1, thus determining individual sets as hypotheses. The hypothesis generation part  32   d  sends hypotheses, generated therein, to the likelihood calculation part  33   d.    
     After step S 7   d , the likelihood generation part  33   d  performs step S 8  and its following steps shown in  FIG. 31 . Herein, a series of steps (i.e. a loop consisting of steps S 8  to S 10 ) for determining existence/nonexistence of hypotheses with uncalculated likelihoods and for calculating track-coupling likelihoods and identification likelihoods is similar to that of Embodiment 1. Upon determining existence of hypotheses without execution of calculation of likelihoods (specifically, steps S 9  to Slid) in step S 8  (i.e. a decision result “Yes” of step S 8 ), the likelihood calculation control part  330   d  sends one of hypotheses with uncalculated likelihoods to the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332 , and the attribute likelihood calculation part  334 . In this connection, calculation of track-coupling likelihoods and identification likelihoods (i.e. steps S 9 , S 10 ) is identical to that of Embodiment 1; hence, the description thereof will be omitted. 
     The attribute likelihood calculation part  334  calculates attribute likelihoods with respect to received hypotheses (step S 54 ). In step S 54 , the attribute likelihood calculation part  334  calculates attribute likelihoods of individual track-coupling candidates ascribed to a hypothesis, multiplies and normalizes attribute likelihoods of track-coupling candidates ascribed to the same hypothesis, thus calculating an attribute likelihood. In this connection, attributes used for calculation of attribute likelihoods are provided from the hypothesis generation part  32  as combinations of attributes, attribute types, and detection times after updating (i.e. after step S 52 ). 
     After step S 54  for calculating attribute likelihoods, the attribute likelihood calculation part  334  integrates track-coupling candidates of hypotheses calculated in step S 9 , identification likelihoods of hypotheses calculated in step S 10 , and attribute likelihoods calculated in step S 54 , thus storing the integration results in connection with hypotheses (step S 11   d ). In step S 11   d , track-coupling likelihoods, identification likelihoods, and attribute likelihoods calculated in steps S 9 , S 10 , and S 54  are multiplied and integrated. In the integration process, the attribute likelihood calculation part  334  may applies weights to track-coupling likelihoods, identification likelihoods, and attribute likelihoods. 
     After step S 11   d , the likelihood calculation part  33   d  repeats a loop consisting of steps S 8  to S 11   d . Upon determining nonexistence of hypotheses with uncalculated likelihoods in step S 8  (i.e. a decision result “No” of step S 8 ), the attribute calculation part  334  estimates a hypothesis with the highest integration value of a track-coupling likelihood, an identification likelihood, and an attribute likelihood as a most-probable hypothesis (step S 12   d ). The attribute likelihood calculation part  334  sends the estimated most-probable hypothesis to the identification result output device  4 . 
     Upon receiving the most-probable hypothesis estimated in step S 12   d , the identification result output device  4  determines which mobile body matches which track with reference to attribute-attached track-coupling candidate/identification pairs ascribed to the most-probable hypothesis (step S 13 ). At this time, the identification result output device  4  may specifically display the position or track of a mobile body. In this connection, step S 13  is identical to that of Embodiment 1. 
     In Embodiment 5, the attribute detection device  5  detects features of mobile bodies as attributes so that the mobile body track identification device  3   d  utilizes attribute likelihoods, in addition to track-coupling likelihoods and identification likelihoods, in calculating likelihoods of hypotheses. When a plurality of mobile bodies moves together along similar paths, a plurality of track-coupling candidate/identification pairs substantially shares the same calculation result of track-coupling likelihoods and identification likelihoods, whereby it is presumed that no differences may occur between likelihoods of hypotheses. Even when a plurality of mobile bodies moves together along similar paths, some differences may occur between likelihoods of hypotheses due to differences between features of mobile bodies since Embodiment 5 utilizes attribute likelihoods in calculating likelihoods of hypotheses; hence, it is possible to estimate which mobile body matches which track. In the integration of likelihoods (step S 11   d ), attribute likelihoods are integrated so as to estimate a most-probable hypothesis according to the integration result; hence, it is possible to estimate which mobile body matches which track with a high precision. 
     Similar to the foregoing variation of Embodiment 1, it is possible for the likelihood calculation part  33   d  of Embodiment not to include the track-coupling likelihood calculation part  331 , thus precluding calculation of track-coupling likelihoods. In this case, the attribute likelihood calculation part  334  may solely integrate identification likelihoods and attribute likelihoods, thus estimating a hypothesis with the highest integration value as a most-probable hypothesis. 
     Similar to Embodiment 2, it is possible for the likelihood calculation part  33   d  of Embodiment 5 not to include the track-coupling likelihood calculation part  331 , so that the mobile body track identification device  3   d  may include the track-coupling likelihood calculation part  331   a . In this case, the attribute likelihood calculation part  334  solely integrates identification likelihoods and attribute likelihoods, thus estimating a hypothesis with the highest integration value as a most-probable hypothesis. This constitution may demonstrate the same effect as the effect of Embodiment 2 in Embodiment 5. 
     Moreover, it is possible to combine Embodiment 3 with Embodiment 5 or its variation. Alternatively, it is possible to combine Embodiment 4 with Embodiment 5 or its variation. Alternatively, it is possible to combine Embodiments 3 and 4 with Embodiment 5 or its variation. 
     Embodiment 6 
       FIG. 32  is a block diagram of a mobile body track identification system according to Embodiment 6 of the present invention. Herein, the same constituent elements as the constituent elements of Embodiment 1 are designated by the same reference numerals; hence, descriptions thereof will be omitted. The mobile body track identification system of Embodiment 6 includes the positional information detection device  1 , the identification information detection device  2 , a mobile body track identification device  3   e , the identification result output device  4 , and a mobility information detection device  6 . The mobile body track identification device  3   e  includes the track-coupling likelihood generation part  31 , a hypothesis generation part  32   e , a likelihood calculation part  33   e , and the map storage  37 . 
     The mobile body track identification system does not necessarily include a single mobility information detection device  6 ; hence, it is possible to include a plurality of mobility information detection devices  6 . A mobility detection device ID is allocated to the mobility information detection device  6  to univocally specify it. In this connection, a specific number is used as the mobility detection device ID; hence, it will be referred to as a mobility detection device number. 
     The mobility information detection device  6  retrieves mobility of a mobile body in the tracking area  50 . The mobility is information representing a moving condition of a mobile body being tracked; hence, it may indicate a moving speed, a moving direction, or the like. 
     The mobility information detection device  6  is a device that is able to detect mobility of a mobile body in the tracking area  50  so as to specify its detection time, wherein it is realized using a camera, a laser range finder, or an acceleration sensor. The mobility information detection device  6  can be arranged inside the tracking area  50 . Alternatively, a mobility body may carry a portable terminal equipped with a sensor with an ability of detecting mobility by itself. 
     When a plurality of mobility information detection devices  6  is installed in the mobile body track identification system, the mobility information detection devices  6  can be arranged such that their detection ranges overlap with each other. Alternatively, the mobility information detection devices  6  can be arranged such that their detection ranges do not overlap with each other. Additionally, it is possible to use a plurality of mobility information detection devices  6  of different types, such as a camera and an acceleration sensor, in common. 
     Even when a mobile body actually exists, the mobility information detection device  6  may not always detect its mobility; hence, a decision as to whether or not mobility is detected depends on the installed condition of the mobility information detection device  6 . When the mobility information detection device  6  is arranged in the tracking area  50 , for example, the mobility information detection device  6  can easily detect mobility of a mobile body positioned close to it, whilst the mobility information detection device  6  may hardly detect mobility of a mobile body distanced from it. When a mobile body carries a portable terminal equipped with an acceleration sensor as the mobility information detection device  6 , in other words, when the mobility information detection device  6  accompanies with a mobile body, the mobility information detection device  6  is able to normally detect mobility of a mobile body. 
     It is presumed that a certain type of the mobility information detection device  6  may not necessarily detect positional coordinates at which mobility is detected. When a portable terminal is equipped with an acceleration sensor as the mobility information detection device  6  so as to detect a moving condition of a mobile body such as “rest”, “walk”, and “run” or to detect “step count” of a mobile body (a person), for example, the mobility information detection device  6  does not detect positional coordinates at which mobility is detected. In this case, the mobility information detection device  6  sets “null” to positional coordinates at which mobility is detected. In the case where no positional coordinates regarding mobility are detected, those positional coordinates are expressed using a character string of “null” in the following description. In this connection, those positional coordinates can be expressed using other character strings. When positional coordinates regarding mobility are detectable, the mobility information detection device  6  detects mobility of a mobile body while detecting positional coordinates of the mobile body at the detection time of mobility. Mobility with positional coordinates specified by a certain value will be referred to as “positional coordinates-attached mobility”. 
     The mobility information detection device  6  sends the detected mobility, a mobility type univocally indicating its type, a detection time of mobility, positional coordinates of mobility, and its mobility detection device number to the mobile body track identification device  3   e . When the mobility information detection device  6  is unable to detect positional coordinates, “null” is set to positional coordinates of mobility. If the mobility information detection device  6  operates to detect mobility but fails to do so, the mobility information detection device  6  may send mobility and its mobility type, which are set to “null”, to the mobile body track identification information device  3   e  together with its time and its mobility detection device number. Alternatively, the mobility information detection device  6  does not send mobility to the mobile body track identification device  3   e  so as to allow the mobile body track identification device  3   e  to determine that no mobility is detected at that time. 
     When the mobility information detection device  6  has a narrow detection range so that positional coordinates of its installed position is deemed equivalent to positional coordinates of a mobile body at the mobility detection time, the mobility information detection device  6  sends positional coordinates thereof to the mobile body track identification device  3   e . In this case, the mobility information detection device  6  needs to hold positional coordinates of the installed position thereof inside the tracking area  50  in advance. 
     When the mobility information detection device  6  detects mobility from an image which is shot using a camera, the installed position of the mobility information detection device  6  may not necessarily match with positional coordinates of a mobile body at the time of extracting mobility from the image due to a broad scope of the shot image. In this case, the mobility information detection device  6  stores a conversion table for converting an arbitrary position, in the shot image, into positional coordinates in the tracking area  50  in advance, so that positional coordinates of a mobile body in its image can be converted into positional coordinates in the tracking area  50  with reference to the conversion table. Alternatively, it is possible to hold a conversion equation, instead of the conversion table, for converting positional coordinates of an arbitrary position in a shot image into positional coordinates in the tracking area  50 , wherein positional coordinates of a mobile body in its image are put into the conversion equation and converted into positional coordinates in the tracking area  50 . 
     As described above, it is possible to determine positional coordinates of a mobile body at the mobility detection time in accordance with a method suited to the type of the mobility information detection device  6  (in other words, the mobility type). 
     It is necessary to establish synchronization between positional information of the positional information detection device  1 , identifications of the identification information detection device  2 , and mobility of the mobility information detection device  6 . That is, the positional information detection device  1  detects positional information of a mobile body; the identification information detection device  2  detects an identification of the mobile body; and the mobility information detection device  6  detects mobility at the same time. When the positional information detection device  1 , the identification information detection device  2 , and the mobility information detection device  6  detect positional information, identifications, and mobility in an asynchronous manner, the mobile body track identification device  3   e  saves positional information, identifications, and mobility for a predetermined time by way of buffering, so that positional information, identifications, and mobility accumulated in a buffer are used every predetermined time. When no timing synchronization is established between the positional information detection device  1 , the identification information detection device  2 , and the mobility information detection device  6 , the mobile body track identification device may set the same detection time with respect to positional information, an identification, and mobility which are received simultaneously. 
     Similar to Embodiment 1, the hypothesis generation part  32   e  of Embodiment 6 receives track-coupling candidates from the track-coupling candidate generation part  31  while receiving identifications from the identification information detection device  2 . Additionally, the hypothesis generation part  32   e  receives mobility, mobility types, detection times, positional coordinates, and mobility detection device numbers, thus holding the correlation between them. 
     Additionally, the hypothesis generation part  32   e  generates all the hypotheses based on track-coupling candidates from the track-coupling candidate generation part  31 , identifications from the identification information detection device  2 , and mobility from the mobility information detection device  6 , thus sending hypotheses, generated therein, to the likelihood calculation part  33   e.    
     Next, a hypothesis generating procedure of Embodiment 6 will be described. 
     First, the hypothesis generation part  32   e  generates combinations of track-coupling candidates and mobility values (i.e. mobility-attached track-coupling candidates) by use of track-coupling candidates from the track-coupling candidate generation part  31  and mobility values which the mobility information detection device  6  detects in a time range retroactive to a predetermined time in the past from the current time. 
     The hypothesis generation part  32   e  selects track-coupling candidates correlated to mobility in accordance with the following procedure, thus generating mobility-attached track-coupling candidates. 
     First, the hypothesis generation part  32   e  correlates mobility values with positional coordinates, among mobility values received from the mobility information detection device  6 , to track-coupling candidates. The hypothesis generation part  32   e  solely extracts mobility values with positional coordinates among mobility values from the mobility information detection device  6 , thus selecting one mobility value therefrom. The hypothesis generation part  32   e  calculates a euclidean distance between positional coordinates of the selected mobility value and positional coordinates of a track-coupling candidate at the detection time of the selected mobility value with respect to all the combinations of track-coupling candidates combined with the selected mobility value. Next, the hypothesis generation part  32   e  specifies and stores a combination of a track-coupling candidate and the selected mobility value with the shortest euclidean distance. This process is performed once with respect to all the mobility values received from the mobility information detection device  6 . Track-coupling candidates correlated to mobility values via this process will be referred to as “temporary mobility-attached track-coupling candidates”. 
     When positional coordinates at the detection time of the selected mobility value cannot be directly detected in connection with a track-coupling candidate with interruption of tracks, the hypothesis generation part  32   e  performs interpolation between interrupted tracks so as to specify positional coordinates of the track-coupling candidate at the mobility detection time, thus calculating a euclidean distance with positional coordinates of mobility. When a track-coupling candidate includes a plurality of tracks at the mobility detection time so that a plurality of positional coordinates, regarding mobile bodies, exists at the mobility detection time, it is necessary to calculate a euclidean distance between a median point and positional coordinates of mobility. This process is similar to the foregoing process of Embodiment 5 for generating attribute-attached track-coupling candidates. 
     Next, the hypothesis generation part  32   e  correlates mobility values with “null” positional coordinates, among the remaining mobility values from the mobility information detection device  6 , to temporary mobility-attached track-coupling candidates. Herein, the hypothesis generation part  32   e  selects only the mobility values with “null” positional coordinates among mobility values from the mobility information detection device  6 , thus generating all the combinations of the selected mobility values and temporary mobility-attached track-coupling candidates. That is, combinations between mobility value with “null” positional coordinate and temporary mobility-attached track-coupling candidates are formulized into “mobility-attached track-coupling candidates”. This process indicates the operation in which mobility values with “null” positional coordinates are added as mobility values correlated to track-coupling candidates. 
     When mobility values with “null” positional coordinates do not exist in mobility values from the mobility information detection device  6 , temporary mobility-attached track-coupling candidates are formulized into “mobility-attached track-coupling candidates”. 
     The above process may allow for existence of track-coupling candidates each correlated to a single mobility value or existence of track-coupling candidates each correlated to a plurality of mobility values. Moreover, the situation in which mobility values with “null” positional coordinates do not exist may allow for existence of track-coupling candidates which are not correlated to mobility values. For instance, track-coupling candidates T 1 , T 2 , . . . , Tn are generated, and mobility values M 1 , M 2 , . . . , Mm with positional coordinates are detected, but mobility values with “null” positional coordinates” do not exist. Herein, nonexistence of mobility values correlated to track-coupling candidates will be expressed using a character string “null mobility”. In this case, the hypothesis generation part  32   e  generates combinations of (T 1 , M 1 ), (T 2 , M 2 ), . . . , (Tn, Mm) or combinations of (T 1 , null mobility), (T 2 , M 1 ), (T 3 , M 2 , M 3 ), . . . , (Tn, Mm- 2 , Mm- 1 , Mm) (i.e. temporary mobility-attached track-coupling candidates). Due to nonexistence of mobility values with “null positional coordinates”, temporary mobility-attached track-coupling candidates are formulized into and held as mobility-attached track-coupling candidates. 
     The hypothesis generation part  32   e  may generate mobility-attached track-coupling candidates such that all the received mobility values are each correlated to any one of track-coupling candidates. The hypothesis generation part  32   e  may generate a temporary mobility-attached track-coupling candidate by determining a track-coupling candidate with the shortest euclidean distance with positional coordinates of mobility, wherein when the euclidean distance exceeds a predetermined threshold, it is possible to determine that the mobility will not be correlated to any one of track-coupling candidates. 
     The hypothesis generation part  32   e  generates all the combinations of mobility-attached track-coupling candidates and identifications by use of mobility-attached track-coupling candidates, which are generated according to the foregoing procedure, and identifications which the identification information detection device  2  has detected in a time range retroactive to a predetermined time in the past from the current time. The hypothesis generation part  32   e  solely focuses on track-coupling candidates among mobility-attached track-coupling candidates, thus generating all the combinations of mobility-attached track-coupling candidates and identifications in accordance with the same process as the foregoing process of generating track-coupling candidate/identification pairs in Embodiment 1. Hereinafter, combinations of mobility-attached track-coupling candidates and identifications will be referred to as mobility-attached track-coupling candidate/identification pairs. 
     Similar to Embodiment 1, the hypothesis generation part  32   e  extracts all the sets of mobility-attached track-coupling candidate/identification pairs satisfying the first to third conditions, thus determining individual sets as hypotheses. 
     The likelihood calculation part  33   e  calculates and integrates a track-coupling likelihood, an identification likelihood, and a mobility likelihood per each hypothesis. It estimates a hypothesis with the highest integration value as a most-probable, thus sending it to the identification result output device  4 . 
     The mobility likelihood is a likelihood in which the same mobile body may be specified by both the mobility correlated to a track-coupling candidate and the moving condition of a mobile body based on the track-coupling candidate. For instance, the mobility likelihood can be defined as a similarity between the moving speed of a mobile body based on its mobility and the moving speed of a mobile body based on a track-coupling candidate. Alternatively, the mobility likelihood can be defined as a similarity between the moving direction of a mobile body based on its mobility and the moving direction of a mobile body based on a track-coupling candidate. 
       FIG. 33  is a block diagram showing the constitution of the likelihood calculation part  33   e  of Embodiment 6. The likelihood calculation part  33   e  includes a likelihood calculation control part  330   e , the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332 , and the mobility likelihood calculation part  335 . Herein, the track-coupling likelihood calculation part  331  and the identification likelihood calculation part  332  are identical to those of Embodiment 1; hence, descriptions thereof will be omitted. 
     The likelihood calculation control part  330   e  sends hypotheses with uncalculated likelihoods to the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332 , and the mobility likelihood calculation part  335 . 
     The mobility likelihood calculation part  335  receives hypotheses, generated by the hypothesis generation part  32   e , so as to calculate mobility likelihoods with respect to those hypotheses. To calculate individual mobility likelihoods, the mobility likelihood calculation part  335  calculates mobility likelihoods of individual track-coupling candidates ascribed to each hypothesis, and then multiplies and normalizes mobility likelihoods of track-coupling candidates ascribed to the same hypothesis, thus calculating a mobility likelihood per each hypothesis. 
     The mobility likelihood calculation part  335  holds a likelihood calculation method per each mobility type in advance, thus determining a calculation method of mobility likelihoods based on the mobility type of each hypothesis. Subsequently, the mobility likelihood calculation part  335  calculates mobility likelihoods in accordance with a calculation method suited to each mobility type. 
     Mobility-attached track-coupling candidates ascribed to a hypothesis may include track-coupling candidates each correlated to a single mobility type or a plurality of mobility types. 
     Next, the processing of the mobility likelihood calculation part  335  for calculating mobility likelihoods in connection with mobility-attached track-coupling candidates will be described. For the sake of simplification of description, the following description refers to the case where each single track-coupling candidate is correlated to a single mobility type. 
     With respect to the mobility type representing a moving direction vector, the mobility likelihood calculation part  335  refers to mobility correlated to track-coupling candidates. Herein, mobility may is expressed using vector V 1  representing a moving direction of a mobile body. The mobility likelihood calculation part  335  calculates vector V 2  representing a moving direction of a mobile body at the mobility detection time based on track-coupling candidates. For instance, the mobility likelihood calculation part  335  may calculate vector V 2  based on positional coordinates of a mobile body before and after its mobility detection time. The mobility likelihood calculation part  335  calculates a cosine similarity between two vectors V 1 , V 2 , thus producing a mobility likelihood. At this time, the mobility likelihood calculation part  335  performs calculation using two vectors V 1 , V 2  according to Equation 1, thus calculating a cosine similarity “cos θ”. 
     In the above example, a similarity between moving direction vectors is calculated by calculating a cosine similarity, thus calculating a mobility likelihood; but it is possible to calculate a similarity between moving direction vectors according to other calculation methods. 
     With respect to the mobility type representing speed of a mobile body, the mobility likelihood calculation part  335  refers to speed serving as mobility correlated to track-coupling candidates. The mobility representing speed may be defined using a numerical value representing moving speed. Alternatively, mobility representing speed may be defined using character strings such as “rest” representing a rest condition, “walk” representing walking speed, and “run” representing speed faster than walking. The mobility information detection device  6  sends mobility which is defined using numerical values or character strings. For instance, the mobility likelihood calculation part  335  extracts positional coordinates of a mobile body at its mobility detection time and before and after the mobility detection time from track-coupling candidates, thus calculating speed of a mobile body. With respect to the mobility representing a numerical value of speed, the mobility likelihood calculation part  335  calculates a difference between speed specified by the mobility and speed calculated based on track-coupling candidates. The mobility likelihood calculation part  335  holds a likelihood function which is defined in advance such that a higher mobility likelihood is produced based on a smaller speed difference whilst a smaller mobility likelihood is produced based on a higher speed difference. The mobility likelihood calculation part  335  puts a difference between speed specified by the mobility and speed calculated based on track-coupling candidates into the likelihood function, thus calculating a mobility likelihood. 
     With respect to the mobility representing speed divisions using character strings such as “rest”, “walk”, and “run”, the mobility likelihood calculation part  335  determines mobility likelihoods according to the following procedure. 
     First, the mobility likelihood calculation part  335  classifies speed calculated based on track-coupling candidates into any one of speed divisions. A certain speed range is set to each speed division in advance. The mobility likelihood calculation part  335  determines mobility likelihoods based on combinations of speed divisions specified by mobility and speed divisions determined using speed calculated based on track-coupling candidates. In this case, the mobility likelihood calculation part  335  holds mobility likelihoods which are defined in advance with respect to combinations of speed divisions. That is, the mobility likelihood calculation part  335  holds mobility likelihoods which are defined in advance with respect to all the combinations of speed divisions calculated based on track-coupling candidates and speed divisions specified by mobility such as [rest, rest], [rest, walk], [rest, run], [walk, walk], [walk, run], and [run, run]. Herein, a higher mobility likelihood is set in connection with similar content of speed divisions, whilst a lower mobility likelihood is set in connection with difference content of speed divisions. For instance, lower mobility likelihoods are determined with respect to combinations of identical speed divisions such as [rest, rest], [walk, walk], and [run, run]. Intermediate mobility likelihoods can be determined with respect to combinations of speed divisions, such as [rest, walk] and [walk, walk], in which speed divisions are not identical to each other but they do not have a big difference in a combination of [rest, run]. The mobility likelihood calculation part  335  selects mobility likelihoods, corresponding to combinations of speed divisions specified by mobility and speed divisions calculated based on track-coupling candidates, from among predefined mobility likelihoods. 
     The above example refers to the case where track-coupling candidates are each correlated to a single mobility value. In the case where track-coupling candidates are each correlated to two or more mobility values, the mobility likelihood calculation part  335  calculates mobility likelihoods regarding track-coupling candidates in accordance with the following procedure. First, the case where track-coupling candidates are each correlated to two mobility values of the same type will be described. 
     The mobility likelihood calculation part  335  sequences mobility values correlated to track-coupling candidates, among mobility-attached track-coupling candidates, in an order of earlier detection times. The mobility likelihood calculation part  335  calculates mobility likelihoods based on mobility values and track-coupling candidates in an order of earlier detection times. The calculation process for calculating mobility likelihoods based on individual mobility values and track-coupling candidates is identical to the foregoing calculation process regarding track-coupling candidates each correlated to a single mobility value. 
     After calculating mobility likelihoods using track-coupling candidates with respect to all mobility values, the mobility likelihood calculation part  335  multiplies mobility likelihoods and then performs normalization for calculating a radical root of the number of mobility likelihoods, thus calculating mobility likelihoods with respect to mobility-attached track-coupling candidates. 
     In the above multiplication process of mobility likelihoods, it is possible to perform weighted multiplication on mobility values in response to the mobility detection device number corresponding to the original mobility used for calculating individual mobility likelihoods. That is, it is possible to multiply mobility likelihoods after a higher weight is applied to mobility likelihoods calculated based on mobility values from a mobility information detection device with a high detection precision whilst a lower weight is applied to mobility likelihoods calculated based on mobility values from a mobility information detection device with a low detection decision. In this connection, when weighted calculation is not necessarily performed in response to the mobility detection device number, the mobility information detection device  6  does not necessarily provide its mobility detection device number. 
     Next, with respect to track-coupling candidates each correlated to mobility of two or more types, the mobility likelihood calculation part  335  calculates mobility values per each mobility type. The calculation process for calculating mobility likelihoods per each single mobility type is identical to the foregoing calculation process regarding track-coupling candidates each correlated to mobility values of the same type. After calculating mobility likelihoods per each mobility type, the mobility likelihood calculation part  335  multiplies mobility likelihoods and then performs normalization for calculating a radical root of the number of mobility likelihoods subjected to multiplication. Thus, it is possible to calculate mobility likelihoods with respect to mobility-attached track-coupling candidates including track-coupling candidates each correlated to two or more types of mobility. In the multiplication of mobility likelihoods calculated per each mobility type, it is possible to apply weight coefficients, corresponding to each mobility type, to mobility likelihoods and then multiply those mobility likelihoods together. 
     With respect to track-coupling candidates correlated with no mobility, the mobility likelihood calculation part  335  sets a constant (e.g. “1”) to mobility likelihoods of those track-coupling candidates. 
     As described above, the mobility likelihood calculation part  335  multiplies and normalizes mobility likelihoods calculated based on track-coupling candidates ascribed to the same hypothesis, thus determined a mobility likelihood of the hypothesis. The normalization calculates a radical root of the number of track-coupling candidates ascribed to the same hypothesis. 
     The integration likelihood calculation part  335  integrates a track-coupling likelihood, an identification likelihood, and a mobility likelihood per each hypothesis. This integration process is realizing by multiplying the track-coupling likelihood, the identification likelihood, and the mobility likelihood. In this integration process, the mobility likelihood calculation part  335  may perform weighted multiplication on the track-coupling likelihood, the identification likelihood, and the mobility likelihood. 
     For instance, the track-coupling candidate generation part  31 , the hypothesis generation part  32   e , and the likelihood calculation part  33   e  (including the likelihood calculation control part  330   e , the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332 , and the mobility likelihood calculation part  335 ) can be realized using a CPU of a computer that operates according to a mobile body track identification program. In this case, a program memory of a computer (not shown) stores the mobile body track identification program so that the CPU loads the program so as to implement the functions of the track-coupling candidate generation part  31 , the hypothesis generation part  32   e , and the likelihood calculation part  33   e  according to the program. Alternatively, it is possible to configure the track-coupling candidate generation part  31 , the hypothesis generation part  32   e , and the likelihood calculation part  33   e  by use of individual hardware elements. Additionally, it is possible to configure the likelihood calculation control part  330   e , the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332 , and the mobility likelihood calculation part  335  by use of individual hardware elements. 
     Next, the operation of the mobile body track identification device  3   e  of Embodiment 6 will be described. 
       FIGS. 34 and 35  are flowcharts showing the processing of the mobile body track identification device  3   e  of Embodiment 6. Herein, the same steps as the steps of Embodiment 1 are designated by the same reference numerals; hence, descriptions thereof will be omitted. In  FIG. 34 , the process of updating the table describing identifications (i.e. steps S 1  to S 5 ) is identical to that of Embodiment 1. 
     The hypothesis generation part  32   e  receives combinations of detection times, mobility, its mobility type, positional coordinates, and mobility detection device numbers from the mobility information detection device  6  (step S 61 ). The mobility information detection device  6  detects mobility of a mobile body so as to send it to the hypothesis generation part  32   e . The hypothesis generation part  32   e  holds the correlation between detection times, mobility, its mobility type, positional coordinates, and mobility detection device numbers in the table. 
     The hypothesis generation part  32   e  holds mobility, which has been collected in a predetermined time in the past from the current time (i.e. the current detection time of the received mobility), received from the mobility information detection device  6 . The hypothesis generation part  32   e  receives the current combination of a detection time, mobility, its mobility type, positional coordinates, and its mobility detection device number so as to additionally register it with the table while deleting the oldest mobility (step S 62 ). For instance, the hypothesis generation part  32   e  has held the correlation between detection times, mobility, its mobility type, positional coordinates, and mobility detection device numbers at times t 1  to tn- 1 . Upon receiving a combination of a detection time, mobility, its mobility type, positional coordinates, and its mobility detection device number at the current time tn, the hypothesis generation part  32   e  stores the mobility regarding the current time tn in the table while discarding the oldest mobility at time t 1  from the table. 
     The hypothesis generation part  32   e  does not only store the updated table by itself but also sends it to the likelihood calculation part  33   e.    
     Moreover, the hypothesis generation part  32   e  generates temporary mobility-attached track-coupling candidates (i.e. combinations of track-coupling candidates and mobility having positional coordinates) by use of mobility stored in the table updated in step S 62  and track-coupling candidates received from the track-coupling candidate generation part  31  in step S 3  (step S 63 ). Specifically, the hypothesis generation part  32   e  chooses only the mobility values with positional coordinates from the received mobility values so as to select one mobility value with positional coordinates. Subsequently, the hypothesis generation part  32   e  calculates a euclidean distance between positional coordinates of the selected mobility value and a track-coupling candidate at the detection time with respect to all the track-coupling candidates combined with the selected mobility value. The hypothesis generation part  32   e  specifies a combination of the mobility value and a track-coupling candidate with the shortest euclidean distance, thus determining it as a temporary mobility-attached track-coupling candidate. This process is performed once with respect to all the mobility values with positional coordinates. With respect to a track-coupling candidate involving interruption of tracks, as described above, the hypothesis generation part  32   e  performs interpolation between interrupted tracks so as to specify positional coordinates of the track-coupling candidate, thus calculating a euclidean distance with positional coordinates of mobility. With respect to a track-coupling candidate including a plurality of tracks, which are detected at the mobility detection time, with a plurality of positional coordinates, a euclidean distance is calculated between a median point and positional coordinates of mobility. 
     In step S 63 , each of track-coupling candidates is not necessarily correlated to a single mobility value with positional coordinates; hence, each of them can be correlated to two or more mobility values with positional coordinates. Additionally, track-coupling candidates not correlated to mobility values with positional coordinates may exist. 
     Then, the hypothesis generation part  32   e  correlate mobility values with “null” positional coordinates (i.e. mobility values which are not selected in step S 63 ) to temporary mobility-attached track-coupling candidates (step S 64 ). In step S 64 , the hypothesis generation part  32   e  generates all combinations of mobility values with “null” positional coordinates and temporary mobility-attached track-coupling candidates, thus formulating them into “mobility-attached track-coupling candidates”. When mobility values with “null” positional coordinates do not exist, the hypothesis generation part  32   e  formulates temporary mobility-attached track-coupling candidates into mobility-attached track-coupling candidates. 
     The hypothesis generation part  32   e  generates all combinations of mobility-attached track-coupling candidate/identification pairs by use of identifications stored in the table updated in step S 5  and mobility-attached track-coupling candidates generated in step S 64  (step S 6   e ). The hypothesis generation part  32   e  solely focuses on track-coupling candidates in mobility-attached track-coupling candidates so as to generate all combinations of mobility-attached track-coupling candidate/identification pairs in the same way as step S 6  of Embodiment 1. 
     Subsequently, the hypothesis generation part  32   e  generates sets of mobility-attached track-coupling candidate/identification pairs as hypotheses (step S 7   e ). In step S 7   e , the hypothesis generation part  32   e  solely focuses on track-coupling candidates and identifications in mobility-attached track-coupling candidate/identification pairs so as to extract all the sets of mobility-attached track-coupling candidate/identification pairs satisfying the first to third conditions as similar to Embodiment 1, thus determining individual sets as hypotheses. The hypothesis generation part  32   e  sends hypotheses, generated therein, to the likelihood calculation part  33   e.    
     After step S 7   e , the likelihood calculation part  33   e  performs a series of steps following step S 8  shown in  FIG. 35 . A series of steps (i.e. steps S 8  to S 10 ) for determining the existence/nonexistence of hypotheses with uncalculated likelihoods and for calculating track-coupling likelihoods and identification likelihoods is identical to that of Embodiment 1. Upon determining existence of hypotheses with uncalculated likelihoods (i.e. hypotheses not subjected to a series of steps S 9  to S 11   e ) in step S 8  (i.e. a decision result “Yes” of step S 8 ), the likelihood calculation control part  330   e  selectively sends one of hypotheses with uncalculated likelihoods to the track-coupling candidate likelihood calculation part  331 , the identification likelihood calculation part  332 , and the mobility likelihood calculation part  335 . Herein, calculation of track-coupling likelihoods and identification likelihoods (steps S 9 , S 10 ) is identical to Embodiment 1; hence, descriptions thereof will be omitted. 
     The mobility likelihood calculation part  335  calculates mobility likelihoods with respect to received hypotheses (step S 65 ). In step S 65 , the mobility likelihood calculation part  335  calculates mobility likelihoods with respect to individual track-coupling candidates ascribed to a hypothesis, calculates and normalizes mobility likelihoods with respect to track-coupling candidates ascribed to the same hypothesis, thus calculating a mobility likelihood of the hypothesis. Mobility values used for calculation of mobility likelihoods are provided from the hypothesis generation part  33   e  as combinations of detection times, mobility values, and mobility types after updating (i.e. after step S 62 ). 
     After step S 65  for calculating mobility likelihoods, the mobility likelihood calculation part  335  integrates a track-coupling likelihood of a hypothesis calculated in step S 9 , an identification likelihood of the hypothesis calculated in step S 10 , and a mobility likelihood calculated in step S 65 , thus storing the integration result in connection with the hypothesis (step S 11   e ). In step S 11   e , the track-coupling likelihood, the identification likelihood, and the mobility likelihood calculated in steps S 9 , S 10 , and S 65  are integrated by way of multiplication. In the integration process, the mobility likelihood calculation part  335  may apply weights to the track-coupling likelihood, the identification likelihood, and the mobility likelihood. 
     After step S 11   e , the likelihood calculation part  33   e  repeats a loop consisting of steps S 8  to S 11   e . Upon determining nonexistence of hypotheses with uncalculated likelihoods in step S 8  (i.e. a decision result “No” of step S 8 ), the mobility likelihood calculation part  335  estimates a hypothesis with the highest integration value between the track-coupling likelihood, the identification likelihood, and the mobility likelihood as a most-probable hypothesis (step S 12   e ). The mobility likelihood calculation part  335  sends the estimated most-probable hypothesis to the identification result output device  4 . 
     Upon receiving the most-probable hypothesis estimated in step S 12   e , the identification result output device  4  determines which mobile body matches which track based on mobility-attached track-coupling candidate/identification pairs ascribed to the most-probable hypothesis (step S 13 ). The identification result output device  4  may specifically display positional coordinates or track of a mobile body. In this connection, step S 13  is identical to that of Embodiment 1. 
     In Embodiment 6, the mobility information detection device  6  detects mobility of a mobile body, so that the mobile body track identification device  3   e  utilizes mobility likelihoods, in addition to track-coupling likelihoods and identification likelihoods, for use in calculation of likelihoods of hypotheses. Even when track-coupling likelihoods and identification likelihoods do not solely contribute to determination of superiority/inferiority with respect to likelihoods of hypotheses and do not achieve accurate estimation as to which mobile body matches with track, mobility, which may represent differences of moving conditions of mobile bodies, may cause differences between likelihoods of hypotheses; hence, it is possible to accurately estimate which mobile body matches with track. Since a most-probable hypothesis is estimated based on the integration result of step S 11   e  involving integration of mobility, it is possible to estimate which mobile body matches which track with a high precision. 
     It is possible to apply the foregoing variation of Embodiment to Embodiment 6, so that the likelihood calculation part  33   d  is reconfigured not to include the track-coupling likelihood calculation part  331  so as not to calculate track-coupling likelihoods. In this case, the mobility likelihood calculation part  335  solely integrate an identification likelihood and a mobility likelihood, thus estimating a hypothesis with the highest integration value as a most-probable hypothesis. 
     In Embodiment 6, the likelihood calculation part  33   e  may not necessarily include the track-coupling likelihood calculation part  331 ; hence, the mobile body track identification device  3   e  is reconfigured to include the track-coupling likelihood calculation part  331   a  as similar to Embodiment 2. In this case, the track-coupling likelihood calculation part  335  solely integrates identification likelihoods and mobility likelihoods so as to estimate a hypothesis with the highest integration value as a most-probable hypothesis. This constitution may demonstrate the same effect as the effect of Embodiment 2. 
     It is possible to combine Embodiment 5 with Embodiment 6 such that the likelihood calculation part  33   e  includes the attribute likelihood calculation part  334  (see  FIG. 29 ) in addition to the track-coupling likelihood calculation part  331 , the identification likelihood calculation part  332 , and the mobility likelihood calculation part  335 . In this case, the mobile body track identification system needs to include the attribute detection device  5  as well. In the integration process of likelihoods, it is possible to integrate track-coupling likelihoods, identification likelihoods, mobility likelihoods, and attribute likelihoods, thus estimating a most-probable hypothesis based on the integration result. 
     Moreover, it is possible to create the constitution combining Embodiment 3 with Embodiment 6 or its variation, or it is possible to create the constitution combining Embodiment 4 with Embodiment 6 or its variation. Alternatively, it is possible to create the constitution combining Embodiments 3 and 4 with Embodiment 6 or its variation. 
     Embodiment 7 
       FIG. 36  is a block diagram of a mobile body track identification system according to Embodiment 7 of the present invention. Herein, the same constituent parts as the constituent parts of Embodiment 1 are designated by the same reference numerals; hence, descriptions thereof will be omitted. The mobile body track identification system of Embodiment 7 includes the positional information detection device  1 , the identification information detection device  2 , a mobile body track identification device  3   f , and the identification result output device  4 . The mobile body track identification device  3   f  of Embodiment 7 includes the track-coupling candidate generation part  31 , a hypothesis generation part  32   f , the likelihood calculation part  33 , the map storage  37 , and an environmental information storage  38 . 
     The environmental information storage  38  stores indexes, representing the position of obstruction that exists in the tracking area  50  and a transient time in which a mobile body passes by the position of obstruction, as environmental information. The following description refers to environmental information including indexes, representing the position of obstruction and a transient time in which a mobile body passes by the position of obstruction, but it is possible to employ other elements as environmental information. Other examples of environmental information will be described later. Additionally, the following description refers to an index representing a transient time, in which a mobile body passes by the position of obstruction, as “cost”. 
     Similar to Embodiment 1, the hypothesis generation part  32   f  receives track-coupling candidates from the track-coupling candidate generation part  31  while receiving identifications from the identification information detection device  2 . Additionally, the hypothesis generation part  32   f  reads environmental information from the environmental information storage  38 . 
     With respect to a track-coupling candidate involving interruption of tracks, the hypothesis generation part  32   f  calculates a transient time of a mobile body during interruption of tracks in the track-coupling candidate based on a track disappearance time and a track appearance time. Hereinafter, this transient time will be referred to as a real transient time. Additionally, the hypothesis generation part  32   f  calculates a moving distance of a mobile body during the real transient time so as to estimate a time in which the mobile body moves in the moving distance based on environmental information. Hereinafter, this estimation value will be referred to as an estimated transient time. The track-coupling candidate, in which the estimated transient time is longer than the real transient time, is discarded from track-coupling candidates used for generation of hypotheses. That is, the hypothesis generation part  32   f  generates hypotheses by use of track-coupling candidates, in which estimated transient times are shorter than real transient times, and track-coupling candidates not involving interruption of tracks. 
     Next, the processing for calculating real transient times and estimated transient times and for determining whether or not track-coupling candidates need to be discarded will be described in conjunction with specific examples of environmental information.  FIG. 37  is a schematic illustration of environmental information. Herein, the tracking area  50  is divided into a plurality of areas, so that costs defined in divided areas are used as environmental information stored in the environmental information storage  38 . Hereinafter, divided areas will be referred to as cells. Specifically, the tracking area is divided into 18×9=192 cells. Herein, cost “0” is set to areas (i.e. while cells in  FIG. 37 ) involving nonexistence of obstruction. Higher costs are set to cells which need longer times for mobile body to pass by due to existence of obstruction. In the tracking area shown in  FIG. 37 , obstacles exist in areas  61 ,  62 . A cost “3.0” is set to a cell corresponding to the center area  61  involving existence of obstruction, whilst a cost “0.5” is set to a cell corresponding to the peripheral area  62  involving existence of obstruction. Cells whose costs are not “0” indicate existence of obstruction. The above setting of environmental information will be referred to as an obstruction map. 
     The hypothesis generation part  32   f  calculates a real transient time from a track disappearance time to a next track appearance time with respect to a track-coupling candidate. 
     Additionally, the hypothesis generation part  32   f  interpolates a straight line between positional coordinates at track disappearance and positional coordinates of next track appearance in a track-coupling candidate. The hypothesis generation part  32   f  calculates positional coordinates, within a range of positional coordinates on the interpolated line, at a detection time (i.e. a detection time of positional information and identifications) of a real transient time (in other words, a track interrupted time). The hypothesis generation part  32   f  calculates the sum of costs set to the cells corresponding to those positional coordinates. 
     The hypothesis generation part  32   f  calculates a distance between a track disappearance time and a next track appearance time in a track-coupling candidate, thus determining the distance as a moving distance a moving distance of a mobile body involving interruption of tracks. The hypothesis generation part  32   f  divides the moving distance of a mobile body involving interruption of tracks by an upper-limit value of the moving speed of a mobile body, and adds the sum of costs to the division result, thus determining the addition result as an estimated transient time. In this connection, the upper-limit value of the moving speed of a mobile body is determined in advance and retained in the hypothesis generation part  32   f.    
     The hypothesis generation part  32   f  performs the above processing on track-coupling candidates involving interruption of tracks. Subsequently, it precludes track-coupling candidates, in which estimated transient times are longer than real transient times, from track-coupling candidates used for generation of hypotheses. 
     Next, a specific calculation method of real transient times and estimated transient times will be described. It focuses on a track-coupling candidate including tracks a, b shown in  FIG. 38 . The track a has a disappearance time t 1  whilst the track b has an appearance time t 6 . Herein, the time t 1  is earlier than the time t 6 . Additionally, the track a has positional coordinates of disappearance (Xa, Ya) whilst the track b has positional coordinates of appearance (Xb, Yb). 
     First, the hypothesis generation part  32   f  calculates a real transient time involving interruption of tracks by way of t 6 −t 1 . 
     Next, the hypothesis generation part  32   f  interpolates a straight line between positional coordinates of disappearance (Xa, Ya) of the track a and positional coordinates of appearance (Xb, Yb) of the track b, thus calculating positional coordinates, within a range of positional coordinates on the line, in detection times t 2  to t 5  that fall within the real transient time. In this connection, costs “0”, “0.5”, “3.0”, and “0.5” are set to the cells at the positions of times t 2 , t 3 , t 4 , and t 5 ; hence, the sum of costs in the interpolation interval is “0+0.5+3.0+0.5=4.0”. 
     The hypothesis generation part  32   f  calculates a moving distance L of a mobile body during interruption of tracks via Equation 5.
 
 L =√{square root over (( Xa−Xb ) 2 +( Ya−Yb ) 2 )}{square root over (( Xa−Xb ) 2 +( Ya−Yb ) 2 )}  [Equation 5]
 
     The hypothesis generation part  32   f  calculates an estimated transient time Tm via Equation 6. 
     
       
         
           
             
               
                 
                   Tm 
                   = 
                   
                     
                       
                         L 
                         ⁢ 
                         
                             
                         
                       
                       
                         V 
                         ⁢ 
                         
                             
                         
                         ⁢ 
                         max 
                       
                     
                     + 
                     C 
                   
                 
               
               
                 
                   [ 
                   
                     Equation 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     6 
                   
                   ] 
                 
               
             
           
         
       
     
     In Equation 6, L denotes the moving distance calculated via Equation 5, and Vmax denotes a value that is determined as the upper-limit value of a moving speed of a mobile body in advance. Additionally, C denotes the sum of costs in an interpolated space, which is “4.0” in  FIG. 38 . 
     The hypothesis generation part  32   f  discards the track-coupling candidate including the tracks a, b when the estimated transient time Tm calculated via Equation 6 is longer than the real transient time t 6 −t 1 . 
     Due to existence of obstruction, higher costs are set to cells that require longer times for a mobile body to pass by in the obstruction map. The costs in  FIG. 38  have a correcting function for increasing a transient time that allows a mobile body to pass through the path, interpolated between the tracks a, b, involving existence of obstruction. 
     A straight line is interpolated between interrupted tracks with respect to track-coupling candidates that are allowed to remain without being discarded, so that positional coordinates are calculated at detection times (times t 2  to t 5  in  FIG. 38 ) in the interpolated portion. By using track-coupling candidates that are allowed to remain without being discarded, the hypothesis generation part  32   f  generates track-coupling candidate/identification pairs, thus generating hypotheses. The generation process of track-coupling candidate/identification pairs and hypotheses is identical to that of Embodiment 1. 
     In the above description, the environmental information storage  38  stores environmental information using indexes (cost) representing the position of obstruction and a transient time in which a mobile body passes by the position of obstruction. The environmental information storage  38  may store a transient time, in which a mobile body moves along a passable path in a tracking area, as environmental information. The processing of the hypothesis generation part  32   f  using this environmental information (i.e. a process of determining whether to discard track-coupling candidates) will be described below. 
     When environmental information uses a transient time of a mobile body along a passable path in a tracking area, the hypothesis generation part  32   f  calculates a real transient time of a mobile body during interruption of tracks based on a track disappearance time and a next track appearance time with respect to a track-coupling candidate involving interruption of tracks. With reference to environmental information, the hypothesis generation part  32   f  calculates an estimated transient time along a passable path, which leads from the proximity of a track disappearance position to the proximity of a track appearance position in a track-coupling candidate. The hypothesis generation part  32   f  discards a track-coupling candidate, which is currently focused on, in which the estimated transient time calculated based on environmental information is longer than the real transient time. 
       FIG. 39  schematically shows environmental information which is applied to a transient time of a mobile body along a passable path in a tracking area. This environmental information locates a plurality of nodes n in areas not involving existence of obstacles Ob serving as obstruction to a mobile body in a tracking area. The nodes n are points representing positional coordinates at which a mobile body may exist in a tracking area with arbitrary resolutions. Additionally, edges e satisfying a condition of not intersecting with obstacles Ob are selected from among edges connecting between the nodes n. Straight lines are used at the edges e.  FIG. 39  shows the setting of nine nodes n, which are allocated with node identification number “1” to “9”. Edges such as an edge e connecting between nodes  1 ,  2  and an edge e connecting between nodes  2 ,  5  are selected as edges not intersecting with the obstacles Ob. Herein, edges such as an edge connecting between nodes  4 ,  5  and an edge connecting between nodes  5 ,  6  are not selected because they intersect with the obstacles Ob. Transient times are set to individual edges e along which a mobile body passes through. In this connection, transient times set to edges will be referred to as costs. In  FIG. 39 , costs are described along with the edges e. For instance, “1” is set to the transient time (cost) in which a mobile body moves along the edge e connecting between nodes  1 ,  4 . Additionally, “1.5” is set to the transient time (cost) in which a mobile body moves along the edge e connecting between nodes  1 ,  2 . The positions of nodes n and the costs of edges e are determined as environmental information representing transient times in passable paths for a mobile body in advance, so that the environmental information is stored in the environmental information storage  38 . This environmental information is defined as a path graph representing the correlation between edges and costs. 
     The hypothesis generation part  32   f  interpolates edges between the proximity of track disappearance position and the proximity of track appearance position in a track-coupling candidate, thus calculating the sum of costs along the interpolated edges as an estimated transient time ranging from the proximity of track disappearance position to the proximity of track appearance position. The hypothesis generation part  32   f  discards a track-coupling candidate, which is currently focused on, in which the estimated transient time (i.e. sum of costs of interpolated edges) is longer than the real transient time. 
     Next, the processing of interpolating edges as paths between tracks will be described. The following description refers to the case where paths are interpolated between the tracks a, b shown in  FIG. 39 . Herein, the appearance time of the track a is earlier so that appearance of the track b is detected after disappearance of the track a. 
     The hypothesis generation part  32   f  calculates distances between positional coordinates of disappearance of the track a and nodes so as to select a node which is the closest to positional coordinates of disappearance of the track a. Similarly, the hypothesis generation part  32   f  calculates distances between positional coordinates of appearance of the track b and nodes so as to select a node which is the closest to positional coordinates of appearance of the track b. In  FIG. 39 , the hypothesis generation part  32   f  selects node  4  which is the closest to positional coordinates of disappearance of the track a while selecting node  5  which is the closest to positional coordinates of appearance of the track b. 
     Next, the hypothesis generation part  32   f  searches for a path connecting between the selected two nodes  4 ,  5  on a graph, thus calculating the sum of costs corresponding to edges included in the searched path. Subsequently, the hypothesis generation part  32   f  discards a track-coupling candidate, which is currently focused on, in which the sum of costs of edges included in the searched path (i.e. an estimated transient time) is larger than a real transient time (i.e. a time between a detection time of disappearance of the track a and a detection time of appearance of the track b). 
     In the case where a plurality of paths connecting between the selected two nodes  4 ,  5  exists, the paths connecting these nodes may have different combinations of interpolated edges. In this case, the hypothesis generation part  32   f  handles paths between the two nodes  4 ,  5  (i.e. combinations of interpolated edges) as different track-coupling candidates, thus comparing the sum of cost of edges with a real transient time per each track-coupling candidate. Subsequently, it discards track-coupling candidates in which the sum of costs of interpolated edges is larger than the real transient time while adopting track-coupling candidates, in which the sum of costs of interpolated edges is lower than or equal to the real transient time, for use in generation of track-coupling candidate/identification pairs and hypotheses. 
     In  FIG. 39 , a “first path” denoted by nodes  4 (R) 1 (R) 2 (R) 5 , a “second path” denoted by nodes  4 (R) 7 (R) 8 (R) 5 , and a “third path” denoted by nodes  4 (R) 1 (R) 2 (R) 3 (R) 6 (R) 9 (R) 8 (R) 5  can be presumed as paths between the two nodes  4 ,  5 . Other paths may exist, but the first to third paths are subjected to consideration. The sum of costs along the first path is “1+1.5+1=3.5”, whilst the sum of costs along the second path is “3.5” as well. Additionally, the sum of costs along the third path is “8.5”. 
     With respect to a real transient time (i.e. a time from a detection time of disappearance of the track a and a detection time of appearance of the track b) less than 3.5, the sum of costs owing to interpolation of any one of the first to third paths becomes larger than the real transient time; hence, the hypothesis generation part  32   f  discards track-coupling candidates including the tracks a, b. 
     With respect to a real transient time that is equal to or above 3.5 but less than 8.5, the sum of costs owing to interpolation of either the first path or the second path is larger than the real transient time, the hypothesis generation part  32   f  adopts a track-coupling candidate interpolating the first path between the tracks a, b and a track-coupling candidate interpolating the second path between the tracks a, b for use in generation of track-coupling candidate/identification pairs and hypotheses. At this time, the hypothesis generation part  32   f  calculates positional coordinates along paths at various detection times (i.e. detection times of positional information, and detection times of identifications) that occur in a real transient time from a detection time of disappearance of the track a and a detection time of appearance of the track b. In this connection, a track-coupling candidate interpolating the third path between the tracks a, b is discarded because the sum of costs along the third path is larger than the real transient time. 
     As shown in  FIG. 39 , numeral paths arbitrarily connecting between two nodes may exist in a path graph configured of closed circuitry. In this case, the upper limit is determined with respect to the cost of an interpolated path, so that paths in which the sum of costs exceeds the upper limit are precluded from the interpolation process between tracks. 
     As described above, the hypothesis generation part  32   f  performs track-coupling candidate search/discard processes by use of the obstruction map (see  FIG. 37 ) as environmental information or the path graph (see  FIG. 39 ) as environmental information. For this reason, the hypothesis generation part  32   f  may determine whether or not to discard track-coupling candidates by use of either one of environmental information, but it is possible to makes a decision whether to discard track-coupling candidates by use of both the environmental information. Alternatively, the hypothesis generation part  32   f  may determine whether to discard track-coupling candidates by use of a different manner of environmental information rather than the obstruction map or the path graph. 
     In this connection, the track-coupling candidate generation part  31 , the hypothesis generation part  32   f , and the likelihood calculation part  33  can be realized using a CPU of a computer that operates according to a mobile body track identification program. In this case, a program memory of a computer (not shown) stores the mobile body track identification program so that the CPU loads the program to achieve the functions of the track-coupling candidate generation part  31 , the hypothesis generation part  32   f , and the likelihood calculation part  33 . 
     Next, the operation of the mobile body track identification device  3   f  of Embodiment 7 will be described. 
       FIG. 40  is a flowchart showing the processing of the mobile body track identification device  3   f  of Embodiment 7. Herein, the same steps as the steps of Embodiment 1 are designated by the same reference numerals; hence, descriptions thereof will be omitted. 
     In  FIG. 40 , a series of steps (i.e. steps S 1  to S 5 ) for updating the table describing identifications is identical to that of Embodiment 1. 
     After step S 5 , the hypothesis generation part  32   f  reads environmental information from the environmental information storage  38  so as to determine whether or not to generate track-coupling candidate/identification pairs and hypotheses with respect to track-coupling candidates involving interruption of tracks (step S 71 ). That is, the hypothesis generation part  32   f  determines whether or not to use track-coupling candidate involving interruption of tracks for generation of hypotheses. The details of step S 71  have been described above. 
     After step S 71 , the hypothesis generation part  32   f  generates track-coupling candidate/identification pairs by use of track-coupling candidates which are allowed to remain without being discarded (step S 6 ). The method of generating track-coupling candidate/identification pairs in Embodiment 7 is identical to that of Embodiment 1. Subsequently, the hypothesis generation part  32   f  generates hypotheses by use of track-coupling candidate/identification pairs (step S 7 ). The method of generation hypotheses in Embodiment 7 is identical to that of Embodiment 1. 
     The processing after generation of hypotheses is identical to the foregoing processing of Embodiment 1 after step S 8  shown in  FIG. 5 ; hence, description thereof will be omitted. 
     Embodiment 7 is able to accurately perform interpolation of track-coupling candidates by use of environmental information stored in the environmental information storage  38 . As a result, it is possible to accurately specify positional coordinates of interpolated portions during calculation of identification likelihoods; hence, it is possible to accurately calculate identification likelihoods. By discarding track-coupling candidates, in which a path interpolated between two tracks overlaps with obstruction, from those for use in generation of hypotheses, it is possible to reduce the number of hypotheses generated by the hypothesis generation part  32   f , thus achieving high-speed processing of calculating likelihoods. 
     Next, variations of Embodiment 7 will be described. That is, it is possible to apply the foregoing variation of Embodiment 1 to Embodiment 7 so that the likelihood calculation part  33  does not include the track-coupling likelihood calculation part  331  so as not to calculate track-coupling likelihoods. In this case, the identification likelihood calculation part  332  estimates a hypothesis with the highest identification likelihood as a most-probable hypothesis. 
     Similar to Embodiment 2, the likelihood calculation part  33  of Embodiment 7 may not include the track-coupling likelihood calculation part  331  whilst the mobile body track identification device  3   f  may include the track-coupling candidate calculation part  331   a . In this case, the identification likelihood calculation part  332  estimates a hypothesis with the highest identification likelihood as a most-probable hypothesis. This constitution can demonstrate the same effect as the effect of Embodiment 2. 
     It is possible to combine Embodiment 5 with Embodiment 7 such that the likelihood calculation part  33  includes the attribute likelihood calculation part  334  in addition to the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , and the identification likelihood calculation part  332 . In this case, the mobile body track identification system needs to include the attribute information detection device  5 . 
     It is possible to combine Embodiment 6 with Embodiment 7 such that the likelihood calculation part  33  includes the mobility likelihood calculation part  335  in addition to the likelihood calculation control part  330 , the track-coupling likelihood calculation part  331 , and the identification likelihood calculation part  332 . In this case, the mobile body track identification system needs to include the mobility information detection device  6 . 
     Moreover, it is possible to realize the constitution combining Embodiment 3 with Embodiment 7 or its variation, and it is possible to realize the constitution combining Embodiment 4 with Embodiment 7 or its variation. Additionally, it is possible to realize the constitution combining Embodiments 3 and 4 with Embodiment 7 or its variation. 
     Next, the minimum constitution of the present invention will be described.  FIG. 41  is a block diagram showing the minimum constitution of a mobile body track identification system of the present invention. The mobile body track identification system of  FIG. 41  includes a track-coupling candidate generation part  91 , a hypothesis generation part  92 , and a likelihood calculation part  93 . 
     The track-coupling candidate generation part  91  (which is equivalent to the track-coupling candidate generation parts  31 ,  31   c ) generates track-coupling candidates corresponding to combinations of tracks which have been detected in a predetermined in the past. 
     The hypothesis generation part  92  (which is equivalent to the hypothesis generation parts  32 ,  32   c ) generates sets of track-coupling candidate/identification pairs combining track-coupling candidates generated by the track-coupling candidate generation part  91  and identifications which have been detected in a predetermined time in the past, thus generating hypotheses corresponding to sets of track-coupling candidate/identification pairs satisfying a predetermined condition (which is equivalent to the first to third conditions). 
     The likelihood calculation part  93  (which is equivalent to the likelihood calculation parts  33 ,  33   a ,  33   b ,  33   c ) selects individual hypotheses so as to calculate identification likelihoods representing likelihoods of detecting identifications with respect to tracks specified by track-coupling candidates included in each of track-coupling candidate/identification pairs ascribed to the selected hypothesis. Subsequently, the likelihood calculation part  93  integrates identification likelihoods so as to calculate an identification likelihood of the selected hypothesis, thus estimating a most-probable hypothesis based on it. 
     Since the minimum constitution uses track-coupling candidates and identifications which have been detected in a predetermined time in the past, it is possible to determine which mobile body matches which detected track with a high precision irrespective of frequent interruption of tracks of a mobile body. 
     Embodiments 1 to 7 and their variations can be summarized in mobile body track identification systems with the following constitutions. 
     (1) A mobile body track identification system includes a track-coupling candidate generation part ( 31 ,  31   c ) that generates track-coupling candidates combining tracks detected in a predetermined time in the past; a hypothesis generation part ( 32 ,  32   c ) that generates hypotheses corresponding to sets of track-coupling candidate/identification pairs, which combine track-coupling candidates generated by the track-coupling candidate generation part with identifications of mobile bodies detected in the predetermined time in the past, satisfying a predetermined condition (first to third conditions); and a likelihood calculation part ( 33 ,  33   a ,  33   b ,  33   c ) which selects individual hypotheses so as to calculate identification likelihoods representing likelihoods of detecting identifications with respect to tracks specified by track-coupling candidates included in each of track-coupling candidate/identification pairs ascribed to the selected hypothesis and which integrates identification likelihoods per each track-coupling candidate/identification pair so as to calculate an identification likelihood of the selected hypothesis, thus estimating a most-probable hypothesis based on it.
 
(2) The mobile body track identification system further includes a map storage ( 37 ) that stores a probability map defining detection probabilities of identifications of mobile bodies at various positions in a tracking area. Herein, the likelihood calculation part includes a likelihood calculation control part ( 330 ) that selects individual hypotheses; an identification likelihood calculation part ( 332 ,  332   b ,  332   c ) which specifies the position of a mobile body based on tracks, indicated by track-coupling candidates, at each of detection times of identifications included in track-coupling candidate/identification pairs ascribed to the selected hypothesis, which specifies a detection probability at the position from the probability map, which calculates identification likelihoods with respect to track-coupling candidate/identification pairs by use of the detection probability, and which integrates identification likelihoods so as to calculate an identification likelihood of the selected hypothesis; and a most-probable estimation part (i.e. the identification likelihood calculation parts  332 ,  332   b ,  332   c , or the attribute likelihood calculation part  334 , or the mobility likelihood calculation part  335 ) that estimates a most-probable hypothesis based on identification likelihoods of hypotheses.
 
(3) The mobile body track identification system further includes a likelihood calculation part ( 34 ) that stores track/identification correlation likelihoods representing likelihoods of correlating tracks to identifications. After the identification likelihood calculation part ( 332   b ) estimates a most-probable hypothesis, a track/identification correlation likelihood is calculated with respect to the current combination of a track and an identification included in track-coupling candidates, corresponding to track-coupling candidate/identification pairs ascribed to the most-probable hypothesis and all the hypotheses generated by the hypothesis generation part, so that the track/identification correlation likelihood is stored in the likelihood storage. To calculate identification likelihoods with respect to track-coupling candidate/identification pairs, an identification likelihood per each track-coupling candidate/identification pair is calculated based on the track/identification correlation likelihood corresponding to the current combination of a track and an identification as well as a detection probability which is specified per each identification detection time.
 
     The above constitution is able to reflect identifications, which had been previously detected before the predetermined time in the past in calculating identification likelihoods by use of track/identification correlation likelihoods; hence, it is possible to further improve a precision of correlating tracks to identifications. 
     (4) The mobile body track identification system further includes a hypothesis storage ( 333 ) that stores all the hypotheses generated by the hypothesis generation part together with a most-probable hypothesis, and an information history variation determination part ( 35 ) that determines whether or not variations occur in identifications detected in a predetermined time in the past. When the information history variation determination part determines that variations occur in retrieved information, the track-coupling candidate generation part generates track-coupling candidates, so that the hypothesis generation part ( 32   c ) generates hypotheses and selects individual hypotheses. On the other hand, when the information history variation determination part determines that no variations occur in retrieved information, individual hypotheses are selected from among hypotheses stored in the hypothesis storage. Additionally, when the information history variation determination part determines that variations occur in retrieved information, a most-probable hypothesis is estimated based on hypotheses generated by the hypothesis generation part, so that all the hypotheses are stored in the hypothesis storage together with the most-probable hypothesis. 
     The above constitution does not repetitively generate the same hypotheses even when the hypothesis generation part repetitively generates the same hypotheses as the previously generated hypotheses; hence, it is possible to reduce the amount of calculation in correlating tracks to identifications. 
     (5) In the mobile body track identification system, the hypothesis generation part correlates attributes of mobile bodies, which have been detected in a predetermined time in the past, to track-coupling candidates so as to generate hypotheses corresponding to sets of track-coupling candidate/identification pairs, combining track-coupling candidates correlated to attributes with identifications detected in the predetermined time in the past, satisfying a predetermined condition. The likelihood calculation part includes an attribute likelihood calculation part ( 334 ) which calculates an attribute likelihood, representing a likelihood of each of attributes, correlated to track-coupling candidates, indicating the same mobile body, per each of track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each hypothesis selected from among hypotheses, wherein it integrates attribute likelihoods per each track-coupling candidate/identification pair so as to calculates an attribute likelihood of the selected hypothesis. Additionally, the most-probable estimation part estimates a most-probable hypothesis based on identification likelihoods and attribute likelihoods of hypotheses.
 
(6) In the mobile body track identification system, the hypothesis generation part generates hypotheses corresponding to sets of track-coupling candidate/identification pairs, combining track-coupling candidates correlated to mobility values of mobile bodies detected in a predetermined time in the past and identifications detected in the predetermined time in the past, satisfying a predetermined conditions. The likelihood calculation part includes a mobility likelihood calculation part ( 335 ) which calculates mobility values correlated to track-coupling candidates and mobility likelihoods representing likelihoods of correlating mobility values, calculated based on track-coupling candidates, to the same mobile body with respect to track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each hypothesis selected from among hypotheses, and then integrates mobility likelihoods per each track-coupling candidate/identification pair, thus calculating a mobility likelihood pertaining to the selected hypothesis. Additionally, the most-probable estimation part estimates a most-probable hypothesis based on identification likelihoods and mobility likelihoods of hypotheses.
 
(7) The likelihood calculation part includes a track-coupling likelihood calculation part ( 331 ) which calculates a track-coupling likelihood representing a likelihood of connecting tracks included in a track-coupling candidate per each of track-coupling candidates included in track-coupling candidate/identification pairs ascribed to each hypothesis selected from among hypotheses generated by the hypothesis generation part. Additionally, the most-probable estimation part estimates a most-probable hypothesis based on identification likelihoods and track-coupling likelihoods of hypotheses.
 
     The above constitution estimates a most-probable hypothesis in consideration of not only identification likelihoods but also track-coupling likelihoods, thus improving a precision of correlating tracks to identifications. 
     (8) The track-coupling likelihood calculation part calculates track-coupling likelihoods based on detection times and positional coordinates which are detected with respect to a mobile body defining its tracks. 
     (9) The track-coupling likelihood calculation part ( 331   a ) calculates a track-coupling likelihood representing a likelihood of connecting tracks included in a track-coupling candidate per each of track-coupling candidates generated by the track-coupling candidate generation part, thus selecting track-coupling candidates whose track-coupling likelihoods are equal to or above a threshold. The hypothesis generation part generates hypotheses by use of the selected track-coupling candidates. 
     The above constitution is able to reduce the number of hypotheses generated by the hypothesis generation part, thus achieving high-speed processing of correlating tracks to identifications. 
     (10) The mobile body track identification system includes an environmental information storage ( 338 ) which stores environmental information by use of indexes representing the position of obstruction which exists in a tracking area of a mobile body and a transient time in which a mobile body passes by the position of obstruction. Herein, the hypothesis generation part calculates a real transient time from a track disappearance detection time to a next track appearance detection time with respect to a track-coupling candidate while estimating a transient time of the mobile body, from a track disappearance time to a next track appearance time, based on environmental information. Track-coupling candidates, in which the estimated transient time is longer than the real transient time, are precluded from track-coupling candidates for used in generation of hypotheses.
 
(11) The environmental information storage stores environmental information representing a transient time in which a mobile body passes by a passable path in a tracking area. Herein, the hypothesis generation part calculates a real transient time from a track disappearance detection time to a next track appearance detection time with respect to each track-coupling candidate while estimating a transient time of a mobile body, from a track disappearance time to a next track appearance time, based on environmental information. Track-coupling candidates, in which the estimated transient time is longer than the real transient time, are precluded from track-coupling candidates for use in generation of hypotheses.
 
     Lastly, the present invention is not necessarily limited to the foregoing embodiments and variations since the present invention may embrace all the mobile body track identification technologies that fall within the scope of the invention defined in the appended claims. 
     INDUSTRIAL APPLICABILITY 
     The present invention is applicable to mobile body track identification technology that determines which mobile body matches which track detected in a tracking area. 
     The present invention is applicable to security technologies in such a way that, for example, when positions of persons who work in offices or factories are correlated to employee numbers inherent in individual persons in tracking, it is possible to determine whether or not each area is accessible in response to security authority per each person, thus realizing an alert function when an unauthorized person enters a restricted area. 
     Additionally, the present invention is applicable to marketing technologies in such a way that trends of shoppers are measured by way of tracking using the correlation between positions of shoppers who are shopping for goods in shopping centers and membership numbers inherent in individual persons. 
     REFERENCE SIGNS LIST 
     
         
           1  positional information detection device 
           2  identification information detection device 
           3  mobile body track identification device 
           4  identification result output device 
           5  attribute information detection device 
           6  mobility information detection device 
           31  track-coupling candidate generation part 
           32  hypothesis generation part 
           33  likelihood calculation part 
           34  likelihood storage 
           37  map storage 
           38  environmental information storage 
           50  tracking area 
           91  track-coupling candidate generation part 
           92  hypothesis generation part 
           93  likelihood calculation part 
           330  likelihood calculation control part 
           331  track-coupling likelihood calculation part 
           332  identification likelihood calculation part 
           333  hypothesis storage 
           334  attribute likelihood calculation part 
           335  mobility likelihood calculation part