Abstract:
Disclosed is a target detection device which can match a moving object in a captured image to an identifier when a plurality of identifiers began to be received in a short time, or when the number of identifiers received was larger than the number of detected position histories. The device ( 100 ) comprises: a feature information extraction unit ( 103 ) which extracts the feature information of at least one moving object imaged in a captured image, a read unit ( 104 ) which reads the identifier of the wireless terminal held by the moving object, a history controller ( 105 ) which links and saves the feature information and the identifier at each time, and a matching unit ( 106 ) which matches the feature information and an identifier stored in the history controller ( 105 ) based on the degree of similarity with the feature information stored in the history controller ( 105 ) and the links between the feature information and the identifier.

Description:
TECHNICAL FIELD 
     The present invention relates to an object detection apparatus and object detection method that mutually associate a moving object such as a person detected in a photographic image and a radio terminal identifier. 
     BACKGROUND ART 
     Heretofore, the technology disclosed in Patent Literature 1 has been known as a technology whereby a moving object such as a person photographed in an open space or the like with no clearly defined entrance and no entrance/exit gate is associated with an identifier of a radio terminal held by that moving object. 
     In a conventional moving object detection system, if the difference between a moving object position detection start time and a radio terminal identifier reception start time is within a predetermined time, a moving object position history (hereinafter referred to for convenience as “position history”) and radio terminal identifier (hereinafter referred to for convenience as “identifier”) are mutually associated. A position history is a history of a position of a moving object captured by a camera or the like and a detection start time thereof. 
     Furthermore, in a conventional moving object detection system, if a plurality of position histories exist in which detection was started within a predetermined time after an identifier reception start time, the position history for which the detection start time is closest to the identifier reception start time is associated with the identifier. 
     For example, assume that a conventional moving object detection system acquires two position histories P 1  and P 2  and one identifier ID 1  as shown in  FIG. 1 . In this case, in a conventional moving object detection system, differences |T P1 −T ID1 | and |T P2 −T ID1 | between respective position history detection start times T P1  and T P2  and identifier reception start time T ID1  are calculated. In the example shown in  FIG. 1 , |T P1 −T ID1 |&gt;|T P2 −T ID1 |, and therefore, in a conventional moving object detection system, position history P 2  is associated with identifier T 1 . 
     CITATION LIST 
     Patent Literature 
     PTL 1 
     
         
         Japanese Patent Application Laid-Open No. 2006-98214 
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     However, since a conventional moving object detection system performs association based on only time differences, it cannot perform association correctly in cases (1) and (2) below. 
     (1) When Reception is Started for a Plurality of Identifiers in a Short Time 
     When a plurality of moving objects move while keeping close to each other, the detection start times of the moving objects and the reception start times of identifiers of radio terminals held by the moving objects will be virtually the same (within a margin of error). 
     For example, consider a case in which two moving objects A and B move while keeping close to each other. As shown in  FIG. 2 , detection start times T P1  and T P2  of two position histories P 1  and P 2 , and reception start times T ID1  and T ID2  of two identifiers ID 1  and ID 2 , respectively, are virtually the same. When there are a plurality of identifiers whose reception start times are the same in this way, a position history cannot be associated with an identifier in a conventional moving object detection system. 
     (2) When a Number of Received Identifiers is Greater than a Number of Detected Position Histories 
     It may be, for example, that a moving object position detection range is different from an identifier reception range. Consequently, it may happen, for example, that of two moving objects A and B, moving object A moves through an area that is outside the position detection range but is within the identifier reception range, while moving object B moves through an area that is within both the position detection range and the identifier reception range. 
     In this case, one position history P 1  and two identifiers ID 1  and ID 2  are acquired, as shown in  FIG. 3 . With a conventional moving object detection system, association cannot be performed at either of reception start times T ID1  and T ID2  of the two identifiers ID 1  and ID 2 . The reason for this is that, since detection start time T P1  of position history P 1  is closest, which of the identifiers position history P 1  should be associated with cannot be determined. 
     Thus, with conventional technology, there is a problem of not being able to perform association of an identifier with a moving object in a photographic image when reception is started for a plurality of identifiers in a short time, or when a number of received identifiers is greater than a number of detected position histories. 
     It is an object of the present invention to provide an object detection apparatus and object detection method that enable association of an identifier with a moving object in a photographic image to be performed even if reception is started for a plurality of identifiers in a short time, or if a number of received identifiers is greater than a number of detected position histories. 
     Solution to Problem 
     An object detection apparatus according to the present invention employs a configuration having: a characteristic information extraction section that extracts characteristic information of at least one moving object shown in a photographic image; a reading section that reads an identifier of a radio terminal held by the moving object; a history management section that stores the characteristic information and the identifier associated on a time-by-time basis; and an association section that mutually associates the characteristic information and the identifier stored in the history management section based on similarity of the characteristic information and the association between the characteristic information and the identifier. 
     An object detection method according to the present invention has: a characteristic information extraction step of extracting characteristic information of at least one moving object shown in a photographic image; a reading step of reading an identifier of a radio terminal held by the moving object; a history management step of storing in memory the characteristic information and the identifier associated on a time-by-time basis; and an association step of mutually associating the characteristic information and the identifier stored in the memory based on similarity of the characteristic information stored in the memory and the association between the characteristic information and the identifier. 
     Advantageous Effects of Invention 
     According to the present invention, by finding an identifier corresponding to characteristic information based on similarity of characteristic information, mutual association of a moving object in a photographic image and an identifier can easily be performed even if reception is started for a plurality of identifiers in a short time, or if a number of received identifiers is greater than a number of detected position histories. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a drawing explaining association of an identifier with a position history according to conventional technology; 
         FIG. 2  is a drawing explaining a problem with conventional technology; 
         FIG. 3  is a drawing explaining a problem with conventional technology; 
         FIG. 4  is a block diagram showing the configuration of an object detection apparatus according to Embodiment 1 of the present invention; 
         FIG. 5  is a drawing showing a system configuration including an object detection apparatus according to Embodiment 1 of the present invention; 
         FIG. 6  is a drawing showing an example of history information managed by a history management section according to Embodiment 1 of the present invention; 
         FIG. 7  is a flowchart showing the operating procedure of an association section according to Embodiment 1 of the present invention; 
         FIG. 8  is a drawing explaining clustering according to Embodiment 1 of the present invention; 
         FIG. 9  is a drawing showing an example of history information managed by a history management section according to Embodiment 1 of the present invention; 
         FIG. 10  is a drawing showing an example of a display image according to Embodiment 1 of the present invention; 
         FIG. 11  is a block diagram showing the configuration of an object detection apparatus according to Embodiment 1 of the present invention; 
         FIG. 12  is a block diagram showing the configuration of an object detection apparatus according to Embodiment 1 of the present invention; 
         FIG. 13  is a drawing showing an example of history information managed by a history management section according to Embodiment 1 of the present invention; 
         FIG. 14  is a block diagram showing the configuration of an object detection apparatus according to Embodiment 2 of the present invention; 
         FIG. 15  is a flowchart showing the operating procedure of an association section according to Embodiment 2 of the present invention; 
         FIG. 16  is a drawing showing an example of a display image according to Embodiment 2 of the present invention; 
         FIG. 17  is a block diagram showing the configuration of an object detection apparatus according to Embodiment 3 of the present invention; 
         FIG. 18  is a block diagram showing the configuration of an object detection apparatus according to Embodiment 3 of the present invention; 
         FIG. 19  is a drawing showing a system configuration including an object detection apparatus according to Embodiment 3 of the present invention; and 
         FIG. 20  is a drawing showing an example of history information managed by a history management section according to Embodiment 3 of the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Now, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the embodiments, cases in which a moving object is a person will be described. Also, each person has a radio terminal capable of transmission of a unique identifier by radio. A radio terminal is an RFID (Radio Frequency Identification) tag, contactless IC card, wireless tag, Bluetooth (registered trademark) terminal, wireless LAN (local area network) terminal, millimeter wave tag, or the like. 
     Embodiment 1 
       FIG. 4  is a block diagram showing the configuration of an object detection apparatus according to Embodiment 1 of the present invention, and  FIG. 5  is a drawing showing a system configuration including an object detection apparatus according to Embodiment 1 of the present invention. 
     Object detection apparatus  100  is mainly composed of photographic section  101 , person clipping section  102 , characteristic information extraction section  103 , reading section  104 , history management section  105 , association section  106 , abnormality output section  107 , image storage section  108 , and display section  109 . Association section  106  has association determination section  151 , clustering section  152 , similarity calculation section  153 , and cluster association section  154 . 
     Photographic section  101  photographs a predetermined area using a camera, and outputs a photographic image to person clipping section  102 , characteristic information extraction section  103 , and image storage section  108 . 
     Person clipping section  102  detects an area in which person  201  is shown (hereinafter referred to as “person area”) from a photographic image output from photographic section  101  at predetermined timing. Then, if a number of persons  201  shown in the photographic image increases or decreases, person clipping section  102  reports information indicating this fact to reading section  104 . Person clipping section  102  also outputs a position of a person area to characteristic information extraction section  103 . Person area detection is implemented by using an existing method. For example, person clipping section  102  detects a person area based on a difference between image information of a past frame (background information) and image information of a present frame using input photographic images. 
     Characteristic information extraction section  103  references information output from person clipping section  102 , extracts all characteristic information of person  201  shown in a photographic image output from photographic section  101 , and outputs the extracted characteristic information to history management section  105 . Characteristic information is represented, for example, as N-dimensional data using a color characteristic amount, brightness gradient characteristic amount, and so forth. Generally, in the image recognition field, principal component analysis is executed on a characteristic vector composed of several hundred dimensions, and a vector reduced to around a hundred dimensions is used as characteristic information. However, in this embodiment, a characteristic vector is described as being 2-dimensional (comprising a first principal component and second principal component obtained by principal component analysis of an N-dimensional vector, for example). Characteristic information is, for example, characteristic information indicating a characteristic of a person&#39;s face, or characteristic information indicating a characteristic of a person&#39;s shape. 
     Reading section  104  has a receiving antenna, and receives a radio wave transmitted from radio terminal  202  held by person  201  at the timing of a report from person clipping section  102 . As explained above, a report from person clipping section  102  is a report of information indicating that a number of persons  201  shown in a photographic image has increased or decreased. At this time, reading section  104  may receive a report from person clipping section  102  and issue an inquiry regarding an identifier to radio terminal  202 . Specifically, provision can be made for reading section  104  to transmit an inquiry signal to radio terminal  202 , and for radio terminal  202  to receive a radio wave transmitted by radio terminal  202  in response to that inquiry, and read an identifier superimposed on the received radio wave. Reading section  104  outputs all acquired identifiers to history management section  105 . As shown in  FIG. 5 , for example, reception range  203  of reading section  104  is set equal to or wider than photographic range  204  of photographic section  101 . 
     History management section  105  has characteristic information extracted by characteristic information extraction section  103  and an identifier read by reading section  104  as input, mutually associates characteristic information and an identifier on a time-by-time basis, and manages these as history information. 
     Association section  106  performs mutual association of characteristic information and an identifier managed as history information by history management section  105 . Then association section  106  outputs information indicating an association result to display section  109 . 
     If association section  106  determines that there is an abnormality, abnormality output section  107  outputs information indicating that result to display section  109 . 
     Image storage section  108  stores a photographic image output from photographic section  101 , with time information attached. Display section  109  displays a photographic image stored in image storage section  108 , with an identifier corresponding to characteristic information of a person shown in the photographic image superimposed. If information indicating an abnormality is input from abnormality output section  107 , display section  109  may display information indicating this abnormality superimposed on the photographic image. 
     Association determination section  151  determines whether or not characteristic information and an identifier of history information can be mutually associated on a one-to-one basis. 
     Specifically, if there is one each of a number of items of characteristic information and a number of identifiers of history information at a predetermined time, association determination section  151  mutually associates characteristic information and identifiers on a one-to-one basis. On the other hand, if at least one of a number of items of characteristic information and a number of identifiers of history information is a plurality at a predetermined time, association determination section  151  determines that characteristic information and identifiers cannot be mutually associated on a one-to-one basis. Furthermore, association determination section  151  issues a trigger for performing clustering processing to clustering section  152 . If association of target characteristic information has been established in past clustering processing (processing that performs classification into subsets), association determination section  151  may mutually associate characteristic information and identifiers on a one-to-one basis using the result of that processing. 
     Also, if a cluster and identifier have been mutually associated on a one-to-one basis by cluster association section  154 , association determination section  151  mutually associates characteristic information and an identifier based on that result. 
     Then association determination section  151  outputs information indicating an association result to display section  109 . 
     On receiving a trigger from association determination section  151 , clustering section  152  takes characteristic information of history information managed by history management section  105  as input, and clusters the characteristic information based on similarity. The actual processing performed by clustering section  152  will be described later herein. 
     When clustering is performed by clustering section  152 , similarity calculation section  153  calculates the similarity of characteristic information. In this embodiment, inter-vector distance is used as similarity. In this embodiment, there are no restrictions on the method of calculating similarity. 
     Cluster association section  154  performs mutual association of a cluster generated by clustering section  152  and an identifier. Then, when a cluster and identifier have been mutually associated on a one-to-one basis, cluster association section  154  outputs information indicating that result to association determination section  151 . On the other hand, when a cluster and identifier cannot be mutually associated on a one-to-one basis, cluster association section  154  outputs information indicating that result to abnormality output section  107 . 
     Next, processing whereby association section  106  performs mutual association of characteristic information and an identifier managed by history management section  105  will be described in detail using the example in  FIG. 6 .  FIG. 6  is a drawing showing an example of history information managed by history management section  105 . 
     In this example, it is assumed that two persons are detected at time t 1 , the characteristic information of these persons is (100,10) and (15,150) respectively, and the radio terminal identifiers detected at that time are ID 11  and ID 22 . It is also assumed that two persons are detected at time t 2 , the characteristic information of these persons is (95,15) and (80,90) respectively, and the radio terminal identifiers detected at that time are ID 22  and ID 33 . 
     If characteristic information and identifiers of a plurality of persons are detected at the same time, characteristic information and identifiers cannot be mutually associated with history information alone at that time. Therefore, in the case of  FIG. 6 , association section  106  performs mutual association of characteristic information and identifiers in accordance with the operating procedure shown in  FIG. 7 , using both time t 1  and time t 2  history information.  FIG. 7  is a flowchart showing the operating procedure of association section  106  according to this embodiment. 
     Step  11 : Cluster Characteristic Information. 
     Clustering section  152  clusters characteristic information for which an inter-vector distance calculated by similarity calculation section  153  is shorter than a predetermined threshold value so as to be classified in the same cluster. In this embodiment, a method is used whereby items of characteristic information at a distance less than or equal to a predetermined threshold value are classified as one cluster, but in the present invention the clustering method is not limited to this. Other known clustering methods are a hierarchical method such as a shortest distance method, and a partitioning optimization method such as a k-means method. 
     In this example, as shown in  FIG. 8 , the inter-vector distance of characteristic information (100,10) at time t 1  and characteristic information (95,15) at time t 2  is shorter than a predetermined threshold value. Therefore, clustering section  152  classifies these items of characteristic information into cluster  1 . Also, clustering section  152  classifies characteristic information (15,150) at time t 1  into cluster  2 , and classifies characteristic information (80,90) at time t 2  into cluster  3 . 
     Step  12 : Create Logical Expressions. 
     Taking a cluster as a variable, cluster association section  154  creates logical expressions representing association conditions between each cluster and an identifier at each time. 
     In this example, cluster association section  154  creates expression 1 through expression 4 below. 
     Characteristic information (100,10) at time t 1  belongs to cluster  1 , with ID 11  and ID 22  being possibilities. This can be represented by expression 1 below.
 
Cluster 1={ID11,ID22}  (Expression 1)
 
     Characteristic information (15,150) at time t 1  belongs to cluster  2 , with ID 11  and ID 22  being possibilities. This can be represented by expression 2 below.
 
Cluster 2={ID11,ID22}  (Expression 2)
 
     Similarly, characteristic information (95,15) at time t 2  belongs to cluster  1 , with ID 22  and ID 33  being possibilities, and characteristic information (80,90) belongs to cluster  3 , with ID 22  and ID 33  being possibilities. This can be represented by expressions 3 and 4 below.
 
Cluster 1={ID22,ID33}  (Expression 3)
 
Cluster 3={ID22,ID33}  (Expression 4)
 
     Step  13 : Solve the Logical Expressions. 
     Cluster association section  154  solves the logical expressions created in step  12 , using logical expression transformation. In the present invention, there are no restrictions on the method of solving logical expressions. Known methods of solving logical expressions include a constraint satisfaction problem solution method (such as a backtracking method), an approximation algorithm, a full search, and so forth. 
     In this example, cluster association section  154  finds an identifier corresponding to each cluster by solving above expression 1 through expression 4. First, from expression 1 and expression 3, a possible cluster  1  identifier is limited to ID 22 . This can be represented by expression 5 below.
 
Cluster 1={ID22}  (Expression 5)
 
     Next, from expression 2 and expression 5, a possible cluster  2  identifier is limited to ID 11 . Also, from expression 4 and expression 5, a possible cluster  3  identifier is limited to ID 33 . 
     Step  14 : Mutually Associate Clusters and Identifiers. 
     Cluster association section  154  performs mutual association of clusters and identifiers based on the results of solving the logical expressions. 
     In this example, as explained in step  13 , possible identifiers of each cluster are limited to one, and therefore clusters and identifiers are mutually associated on a one-to-one basis (step  14 : YES). 
     On the other hand, if a person who does not have a radio terminal enters the photographic range, or if radio terminals are exchanged, logical expressions created from history information will conflict. In such a case, identifiers are not associated with clusters when logical expressions are solved (step  14 : NO). 
     Step  15 : Mutually Associate Characteristic Information and Identifiers. 
     If clusters and identifiers have been mutually associated on a one-to-one basis in step  14  (step  14 : YES), association determination section  151  associates an identifier with characteristic information based on a correspondence relationship between clusters and identifiers. Furthermore, association determination section  151  outputs information indicating association results to display section  109 . 
     In this example, from the results of step  13 , cluster association section  154  associates ID 22  with characteristic information (100,10) and characteristic information (95,15) belonging to cluster  1 . Furthermore, cluster association section  154  associates ID 11  with characteristic information (15,150) belonging to cluster  2 , and associates ID 33  with characteristic information (80,90) belonging to cluster  3 . 
     Step  16 : Perform Abnormality Occurrence Processing. 
     If identifiers have not been associated with clusters in step  14  (step  14 : NO), abnormality output section  107  determines that there is an abnormality, and outputs the determination result to display section  109 . By this means, an abnormality comprising non-possession or exchanging of a radio terminal can be detected. 
     Next, a method for performing display for specific characteristic information will be described. 
     When characteristic information extraction section  103  extracts characteristic information of a human object from a photographic image, in addition to a characteristic vector, characteristic information extraction section  103  also extracts the position of an object within an image. Then characteristic information extraction section  103  records a characteristic vector and object position in history management section  105 .  FIG. 9  is a drawing showing an example of history information including intra-image object position information. 
     In the example in  FIG. 9 , it is shown that two objects with characteristic information ((100,10), (300,400)) and characteristic information ((100,10), (300,400)), were detected from a photographic image at time t 1 . The meaning of characteristic information ((100,10), (300,400)) is that (100,10) is a characteristic vector of an object, and (300,400) is a position of an object within an image. 
     Display section  109  performs the following processing when performing display for specific characteristic information. 
     (1) Display section  109  acquires an image of a time corresponding to characteristic information. 
     (2) Display section  109  displays an image acquired from image storage section  108 , superimposed on position coordinate information of an object acquired from characteristic information. 
       FIG. 10  is a drawing showing an example of a display image displayed with identifiers superimposed on a photographic image of time t 1  in  FIG. 6 . Using results found by association section  106 , display section  109  performs display on the periphery of a person area captured within a photographic image such that an identifier of a radio terminal held by that person is known. In the example in  FIG. 10 , display section  109  displays “ID 11 ” and an arrow on the periphery of a person area corresponding to characteristic information (15,150), and displays “ID 22 ” and an arrow on the periphery of a person area corresponding to characteristic information (100,10). 
     As described above, according to this embodiment, characteristic information is clustered, and an identifier corresponding to each cluster is found. By this means, mutual association of a moving object and an identifier can be performed easily even if reception is started for a plurality of identifiers in a short time, or if a number of received identifiers is greater than a number of detected position histories. 
     Also, a problem with conventional technology is that a radio terminal must constantly transmit a radio wave in order for a position history to be ascertained, resulting in high power consumption. However, with this embodiment, reading section  104  need only perform communication with radio terminal  202  when person clipping section  102  detects an increase in the number of persons shown in a photographic image, and therefore it is not necessary for radio terminal  202  to constantly transmit a radio wave. Consequently, radio terminal  202  does not normally perform communication with reading section  104 , but is activated on receiving a radio wave from reading section  104 . Therefore, it is possible to use a semi-passive radio terminal (semi-passive tag) that superimposes an identifier on a radio wave using its own power after activation as radio terminal  202 . Alternatively, it is possible to use a passive radio terminal (passive tag) that superimposes an identifier on a radio wave that reflects part of a radio wave from reading section  104  as radio terminal  202 . By such means, radio terminal power consumption can be kept lower than in the case of conventional technology. 
     In this embodiment, association section  106  may also employ a configuration having number-of-clusters deciding section  155  inside association section  106 , as shown in  FIG. 11 . Number-of-clusters deciding section  155  finds the total number of identifiers of history information managed by history management section  105 , and outputs the result to clustering section  152  as a maximum number of clusters. In the example in  FIG. 6 , there are three identifiers—ID 11 , ID 22 , and ID 33 —and therefore the maximum number of clusters is set as “3”. If only a period corresponding to part of the history information managed by history management section  105  is made a target of clustering, number-of-clusters deciding section  155  may find the total number of clusters detected in that period, and output the found total to clustering section  152  as the maximum number of clusters. 
     In this case, clustering section  152  sets an upper limit of the number of clusters to the maximum number of clusters when clustering is performed in step  11  in  FIG. 7 . By this means, it is possible to reduce the search range in clustering processing, and the amount of clustering calculation can be reduced and clustering precision improved. 
     Provision may also be made for identifier change detection section  111  to be used instead of person clipping section  102 , as shown in  FIG. 12 . Identifier change detection section  111  monitors reading section  104  reading results, and when the number of identifiers increases or decreases, issues a directive to characteristic information extraction section  103  and reading section  104  to record characteristic information and identifiers in history management section  105 . If photographic section  101  is not constantly performing photography at this time, identifier change detection section  111  also issues a directive to start photography to photographic section  101 . Identifier change detection section  111  outputs a directive to start photography prior to issuing a directive to characteristic information extraction section  103 . 
     Also, in this embodiment, association section  106  may output an association result to history management section  105  as association information, and manage association information. By this means, when performing association from the second time onward, association section  106  can reduce the amount of clustering and association calculation by utilizing association information. This case is described below. 
       FIG. 13  is a drawing showing an example of history data managed by history management section  105 . In this example, this is data in which results of association performed by association section  106  are reflected. The first line of the data table shows that characteristic information (100,10) is associated with ID 22 . Similarly, the data table shows that characteristic information (15,150) is associated with ID 11 , characteristic information (95,15) with ID 22 , and characteristic information (80,90) with ID 33 . 
     Thus, when a request is made to find an identifier corresponding to characteristic information (100,10) at time t 1 , for example, association determination section  151  already knows that characteristic information (100,10) at time t 1  is associated with ID 22 . Consequently, an association result indicating that the identifier is ID 22  can be issued without performing clustering section  152  and subsequent processing. 
     Also, in clustering section  152  and subsequent processing, logical expressions are simplified, enabling the amount of calculation to be reduced. 
     In this embodiment, a case has been described in which association determination section  151  first determines whether or not characteristic information and an identifier can be mutually associated at a single time. However, the present invention is not limited to this, and provision may also be made for association determination section  151  to be eliminated from  FIG. 4 , and for clustering to be performed in all cases. 
     Embodiment 2 
       FIG. 14  is a block diagram showing the configuration of an object detection apparatus according to Embodiment 2 of the present invention. Configuration parts in object detection apparatus  300  shown in  FIG. 14  that are common to object detection apparatus  100  shown in  FIG. 4  are assigned the same reference codes as in  FIG. 4 , and detailed descriptions thereof are omitted here. 
     In Embodiment 2, the procedure for mutual association of characteristic information and an identifier differs from that in Embodiment 1. The internal configuration of association section  306  of object detection apparatus  300  shown in  FIG. 14  differs from that of association section  106  of object detection apparatus  100  shown in  FIG. 4 . 
     Association section  306  has association determination section  351 , close characteristic information adding section  352 , and similarity calculation section  153 , and finds an identifier that is associated with specified characteristic information. 
     Association determination section  351  determines whether or not an identifier with which there is a possibility of one or more items of characteristic information that are determination targets being associated in common can be limited to one, and outputs the result of this determination. 
     Close characteristic information adding section  352  finds characteristic information whose similarity is close for specified characteristic information, and outputs this to association determination section  351 . 
     Next, an actual case will be described in which association section  306  finds an identifier that is associated with specified characteristic information from among characteristic information managed by history management section  105 . 
     Here, it is assumed that history information managed by history management section  105  is similar to that in  FIG. 6  given as an example in Embodiment 1, and that association section  306  is requested to find an identifier of characteristic information (100,10). 
     In this case, association section  306  performs mutual association of specified characteristic information (100,10) and an identifier in accordance with the operating procedure shown in  FIG. 15 .  FIG. 15  is a flowchart showing the operating procedure of association section  306  according to this embodiment. 
     Step  21 : Determine Possibility of Association. 
     Association determination section  351  determines whether or not the number of identifiers with which there is a possibility of characteristic information being associated can be limited to one. 
     In this example, there are two identifiers—ID 11  and ID 22 —for which there is a possibility of corresponding to characteristic information (100,10), and association determination section  351  cannot limit the number of identifiers to one (step  21 : NO). 
     Step  22 : Select Close Characteristic Information. 
     If the number of identifiers cannot be limited to one in step  21  (step  21 : NO), close characteristic information adding section  352  selects close characteristic information based on similarity calculated by similarity calculation section  153 . 
     In this example, close characteristic information adding section  352  selects characteristic information (95,15) closest to characteristic information (100,10). 
     Step  23 : Determine Possibility of Association. 
     Association determination section  351  adds the characteristic information selected by close characteristic information adding section  352  as a determination target, and determines whether or not the number of identifiers common to all characteristic information that is a determination target can be limited to one. 
     In this example, identifiers for which there is a possibility of corresponding to characteristic information (95,15) are ID 22  and ID 33 . Thus, an identifier to which there is a possibility of characteristic information (100,10) and characteristic information (95,15) both corresponding is ID 22  only, and association determination section  351  can limit the number of identifiers to one (step  23 : YES). 
     Step  24 : Mutually Associate Characteristic Information and an Identifier. 
     If the number of identifiers can be limited to one in step  21  or step  23  (step  21 : YES, step  23 : YES), association determination section  351  associates a found identifier with specified characteristic information. Furthermore, association determination section  351  outputs information indicating the association result to display section  109 . 
     In this example, association determination section  351  associates ID 22  with characteristic information (100,10) and characteristic information (95,15). 
     Step  25 : Perform Abnormality Determination. 
     If the number of identifiers cannot be limited to one in step  23  (step  23 : NO), in step  5  association determination section  351  determines whether or not the number of identifiers common to one or more items of characteristic information is zero. 
     Then, if the number of common identifiers is not zero, close characteristic information adding section  352  and association determination section  351  repeat the processing in steps  22  and  23  (step  25 : NO). 
     Step  26 : Perform Abnormality Occurrence Processing. 
     If a person who does not have a radio terminal enters the photographic range, or if radio terminals are exchanged, common identifiers may not exist (step  25 : YES). In this case, abnormality output section  107  determines that there is an abnormality, and outputs the determination result to display section  109 . By this means, an abnormality comprising non-possession or exchanging of a radio terminal can be detected. 
       FIG. 16  is a drawing showing an example of a display image in which an identifier of specified characteristic information is displayed in superimposed fashion. Using results found by association section  306 , display section  109  performs display on the periphery of specified characteristic information captured within a photographic image such that an identifier of a radio terminal held by that person is known. In the example in  FIG. 16 , display section  109  displays “ID 22 ” and an arrow on the periphery of a person area corresponding to characteristic information (100,10). 
     As described above, according to this embodiment, it is possible to find only an identifier corresponding to specified characteristic information. By this means, the amount of calculation can be reduced compared with the method shown in Embodiment 1 in which characteristic information and identifiers are mutually associated en bloc. The method in Embodiment 1 or Embodiment 2 can be used according to the situation. 
     With the present invention, the methods of mutually associating characteristic information and identifiers described in Embodiment 1 and Embodiment 2 may also be switched around as convenient. 
     Embodiment 3 
       FIG. 17  and  FIG. 18  are block diagrams showing configurations of object detection apparatuses according to Embodiment 3 of the present invention, and  FIG. 19  is a drawing showing a system configuration including an object detection apparatus according to Embodiment 3 of the present invention. Configuration parts in object detection apparatus  400  shown in  FIG. 17  that are common to object detection apparatus  100  shown in  FIG. 4  are assigned the same reference codes as in  FIG. 4 , and detailed descriptions thereof are omitted here. Also, configuration parts in object detection apparatus  500  shown in  FIG. 18  that are common to object detection apparatus  300  shown in  FIG. 14  are assigned the same reference codes as in  FIG. 14 , and detailed descriptions thereof are omitted here.  FIG. 19  shows a configuration in which one object detection apparatus  400  has a plurality of photographic sections  1001  through  1003 , a plurality of reading sections  1011  through  1013 , and object detection apparatus  100 . Object detection apparatus  100  performs mutual association of characteristic information and identifiers from photographic sections  1001  through  1003  and reading sections  1011  through  1013 . 
     Object detection apparatus  400  shown in  FIG. 17  has a configuration in which, in comparison with object detection apparatus  100  shown in  FIG. 4 , similarity weighting section  156  has been added inside association section  106 . Object detection apparatus  500  shown in  FIG. 18  has a configuration in which, in comparison with object detection apparatus  300  shown in  FIG. 14 , similarity weighting section  156  has been added inside association section  306 . 
     Similarity weighting section  156  calculates a weighting coefficient, and outputs this to similarity calculation section  153 . 
     Similarity calculation section  153  corrects an inter-vector distance by multiplying a calculated characteristic information inter-vector distance by a weighting coefficient output from similarity weighting section  156 . 
     An example of a weighting coefficient calculation method used by similarity weighting section  156  is described below. 
     With images captured in different time periods, photographic conditions differ, including changes in lighting, dress, hairstyle, and so forth. Therefore, even with characteristic information of the same person, characteristic information extracted from photographic images of different time periods may show an increase in an inter-vector distance. 
     Thus, similarity weighting section  156  calculates a weighting coefficient so that an inter-vector distance decreases the farther apart time periods in which characteristic information that is a target of inter-vector distance calculation is captured are. 
     By this means, the similarity of characteristic information of the same person extracted from images captured in different time periods can be determined more accurately. 
     Also, photographic conditions differ, including changes in lighting, for photographic images captured with different cameras. Therefore, even with characteristic information of the same person, characteristic information extracted from photographic images of different cameras may show an increase in an inter-vector distance. 
     Thus, similarity weighting section  156  calculates a weighting coefficient based on photographic conditions. Photographic conditions include, for example, a camera ID and white balance information for the time when an image was captured. In this case, characteristic information extraction section  103  acquires a camera ID and white balance information from photographic section  101 , and records these in history management section  105  together with characteristic information. An example of a data format managed by history management section  105  including camera IDs and white balance information is shown in  FIG. 20 . 
     By this means, the similarity of characteristic information of the same person extracted from images captured by different cameras can be determined more accurately. 
     Even over a period of several days, a person&#39;s appearance may change significantly, and a characteristic information inter-vector distance may increase. 
     Thus, provision is made for similarity weighting section  156  to be able to make a conjecture using only history information of a fixed time period in which a person&#39;s appearance does not change significantly. For this purpose, similarity weighting section  156  sets a weighting coefficient to a maximum value if a difference in times at which characteristic information that is a target of inter-vector distance calculation is captured exceeds a predetermined threshold value. 
     By this means, it is possible to prevent similarity precision from decreasing due to a significant change in the appearance of the same person. 
     In the above embodiments, a plurality of pairs of photographic section  101  and reading section  104  that receives a radio wave in a range including the photographic range of that photographic section  101  may also be used. In this case, history management section  105  adds the fact that data is data obtained via photographic section  101  and reading section  104  that photograph and receive the same range as additional information, and manages one or more items of characteristic information and one or more identifiers as history information. 
     In the above embodiments, cases have been described in which a moving object is a person, but the present invention is not limited to this, and can also be applied to a moving object other than a person. 
     The present invention can also be applied to a digital camera. In this case, it is possible to identify an ID of a person shown when a photograph is taken by providing a digital camera with object detection apparatus functions used in the above embodiments. By this means, the present invention can increase the added value of a digital camera through combination with an application that automatically sends a taken photograph to an address corresponding to an ID of a person photographed. 
     The present invention can also be applied to a marketing application used in marketing activities by a supermarket, department store, or the like. An example of the use of a marketing application is the installation in a store of an object detection apparatus that detects a user&#39;s ID associated with a movement history of that user. 
     It is possible for the object detection apparatuses used in the above embodiments to be implemented by means of a general-purpose computer such as a personal computer. In this case, the various kinds of processing, including association section processing, are implemented, for example, by having a CPU read and execute a corresponding software program stored in the computer&#39;s memory. The various kinds of processing, including association section processing, may also be implemented by a dedicated device incorporating a corresponding LSI chip. 
     The disclosure of Japanese Patent Application No. 2008-206111, filed on Aug. 8, 2008, including the specification, drawings and abstract, is incorporated herein by reference in its entirety. 
     INDUSTRIAL APPLICABILITY 
     The present invention is suitable for use in an object detection apparatus that mutually associates a moving object such as a person detected within a photographic image and an identifier of a radio terminal. 
     REFERENCE SIGNS LIST 
     
         
           100 ,  300 ,  400 ,  500  Object detection apparatus 
           101 ,  1001 ,  1003  Photographic section 
           102  Person clipping section 
           103  Characteristic information extraction section 
           104 ,  1011 ,  1013  Reading section 
           105  History management section 
           106 ,  306  Association section 
           107  Abnormality output section 
           108  Image storage section 
           109  Display section 
           151 ,  351  Association determination section 
           152  Clustering section 
           153  Similarity calculation section 
           154  Cluster association section 
           155  Number-of-clusters deciding section 
           156  Similarity weighting section 
           352  Close characteristic information adding section