Patent Publication Number: US-2017357855-A1

Title: Information processing apparatus, information processing method, and storage medium

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates to an information processing apparatus, an information processing method, and a storage medium of storing a program related thereto. 
     Description of the Related Art 
     In recent years, the number and kinds of systems which deliver camera images using networks increases. Such image delivering systems are also adopted for Internet sites which deliver the situations of ski resorts and zoos, surveillance or monitoring in shops, buildings and parking lots, and the like. 
     Japanese Patent Application Laid-Open No. 2006-11728 discloses the suspicious person countermeasure system. In this system, the data showing the feature of the face of a person is stored together with the attribute showing a secure person or the like, and the relevant person is identified on the basis of the imaged and captured face and the stored feature of the face. Then, it is decided, based on the attribute of the identified person, whether or not to perform a suspicious person countermeasure in regard to the identified person. 
     However, in such a parking lot as being normally used by unspecified persons, since it is impossible to previously register the features and attributes of persons using the parking lot, there is a problem that it is difficult to determine whether or not a target person is a suspicious person. 
     SUMMARY OF THE INVENTION 
     The present invention has been completed in view of the above problem, and has, for example, the following constitution in order to facilitate determination as to whether or not a target person is a suspicious person. 
     That is, the present invention comprises a person detecting unit configured to detect a person from a captured image captured by one or a plurality of imaging units, a vehicle detecting unit configured to detect a vehicle from the captured image, and an associating unit configured to, in a case where the person detected by the person detecting unit satisfies a predetermined condition of being assumed to be an owner of the vehicle detected by the vehicle detecting unit, associate the detected person with the detected vehicle. 
     Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a general view of a surveillance system. 
         FIGS. 2A and 2B  are diagrams for respectively describing constitutions of an imaging device and a VCA (video content analysis). 
         FIG. 3  is a diagram for describing the functional constitution of the VCA. 
         FIGS. 4A and 4B  are diagrams for describing an example of data configurations of tables. 
         FIG. 5  is a flow chart for describing a data registering process. 
         FIGS. 6A and 6B  are explanatory diagrams for describing a process of identifying a stop position of a vehicle. 
         FIGS. 7A, 7B and 7C  are explanatory diagrams of the data registering process. 
         FIGS. 8A and 8B  are explanatory diagrams of the data registering process. 
         FIGS. 9A, 9B and 9C  are explanatory diagrams of another example of a process of identifying a person to be associated. 
         FIG. 10  is an explanatory diagram of another example of the process of identifying the person to be associated. 
         FIG. 11  is a flow chart for describing a registration data erasing process. 
         FIG. 12  is a flow chart for describing a suspicious person detecting process. 
         FIG. 13  is a diagram for describing an example of a detection area. 
         FIGS. 14A and 14B  are explanatory diagrams of the suspicious person detecting process. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings. 
       FIG. 1  is a general view of a surveillance (or monitoring) system  100  according to the present embodiment. In the present embodiment, a case where the surveillance system  100  surveils (or monitors) a parking lot  150  will be described as an example. The surveillance system  100  comprises a plurality of imaging devices  110 . Each imaging device  110  is a network camera, and is installed in the parking lot  150  as a surveillance area. The installation position, the height and the imaging direction of each imaging device  110  are managed by a later-described VCA (video content analysis)  120 . Although the two imaging devices  110  are illustrated in  FIG. 1 , the number of the imaging devices  110  may be equivalent to one or to three or more. 
     The imaging device  110  is connected to the VCA  120  via a network  160 . Here, it is assumed that the network  160  is a LAN (local area network). A video image captured (imaged) by the imaging device  110  is delivered to the VCA  120  via the LAN  160 . The VCA  120  receives the video image from the imaging device  110 , analyzes the received video image in the parking lot  150 , and thus analyzes suspicious behavior. Besides, the VCA  120  has a function of recording the video image received from the imaging device  110 , and a function of delivering data related to the received video image to a terminal device  130  via a network  170 . The VCA  120  is an example of an information processing apparatus. 
     The terminal device  130  is a client terminal of the VCA  120 . Although it is assumed that the terminal device  130  is a PC (personal computer) in the present embodiment, a portable terminal device such as a smartphone or the like may be used as another example. The VCA  120  delivers the video image to the terminal device  130 , and, when detecting the suspicious behavior from the video image, notifies the terminal device  130  of a suspicious behavior detection. After receiving the notification of the suspicious behavior detection, the terminal device  130  may turn on a search light (not illustrated) installed in the parking lot  150  or issue a warning (alarm). 
     Incidentally, the imaging device  110  and the VCA  120  in the present embodiment perform communication prescribed by the imaging device  110 . Although the LAN  160  and the network  170  are used in the present embodiment, the imaging device  110 , the VCA  120  and the terminal device  130  may be connected to a same network. Communication to be performed among the imaging device  110 , the VCA  120  and the terminal device  130  is irrespective of its communication standard, communication scale and communication configuration. For example, each of the networks  160  and  170  may be the Internet, a wired LAN, a wireless LAN, a WAN (wide area network), a telephone communication line, or the like. Incidentally, the imaging device  110  according to the present embodiment may correspond to, for example, PoE (Power Over Ethernet (registered trademark)), and, in this case, the imaging device is supplied with electric power via a LAN cable. 
       FIG. 2A  is a diagram for describing the constitution of the imaging device  110 . The imaging device  110  comprises a CPU (central processing unit)  201 , a ROM (read only memory)  202 , a RAM (random access memory)  203 , a communicating unit  204  and an imaging unit  205 . The CPU  201  reads control programs stored in the ROM  202 , and performs various processes based on the read programs. The RAM  203  is used as temporary storage areas such as a main memory, a working area and the like of the CPU  201 . The communicating unit  204  performs a communication process with an external device via a network. The imaging unit  205  captures an image (video image) of the surveillance area. Incidentally, the later-described function and process of the imaging device  110  are realized on the premise that the CPU  201  reads a program stored in the ROM  202  and executes the read program. 
       FIG. 2B  is a diagram for describing the constitution of the VCA  120 . The VCA  120  comprises a CPU  211 , a ROM  212 , a RAM  213 , a communicating unit  214  and an HDD (hard disk drive)  215 . The CPU  211 , the ROM  212 , the RAM  213  and the communicating unit  214  are substantially the same as the CPU  201 , the ROM  202 , the RAM  203  and the communicating unit  204 , respectively. The HDD  215  stores various data, various programs and the like. As well as the imaging device  110 , the later-described function and process of the VCA  120  are realized on the premise that the CPU  211  reads a program stored in the ROM  212  or the HDD  215  and executes the read program. Incidentally, the hardware constitution of the terminal device  130  is similar to the hardware constitution of the VCA  120 . However, it is assumed that the terminal device  130  further comprises a displaying unit and an inputting unit. 
       FIG. 3  is a diagram for describing the functional constitution of the VCA  120 . The VCA  120  comprises a communication processing unit  301 , a recording unit  302 , a vehicle detecting unit  303 , a person detecting unit  304 , an identifying unit  305 , a registering unit  306 , a table storing unit  307 , and a determining unit  308 . The communication processing unit  301  controls communication with an external device. For example, the communication processing unit  301  receives a video image as a captured image from the imaging device  110 . The communication processing unit  301  also delivers the received video image to the terminal device  130 . For example, the recording unit  302  records the video image received by the communication processing unit  301 , in a storing unit such as the HDD  215 . 
     The vehicle detecting unit  303  detects a vehicle by applying pattern matching to a moving object in the video image received by the communication processing unit  301 . When the vehicle is detected, the vehicle detecting unit  303  gives a vehicle ID to the detected vehicle, and tracks the relevant vehicle. Further, when the vehicle stops, the vehicle detecting unit  303  extracts a feature amount of the vehicle (hereinafter referred to as a vehicle feature amount). For example, the vehicle feature amount includes information of a height of the vehicle, information of a shape of the vehicle in the captured image at the time when the vehicle is stopped, information of a license plate of the vehicle, and the like. 
     The person detecting unit  304  detects a person by applying pattern matching to a moving object in the video image. For example, the person detecting unit  304  detects the person based on the shape of the person, and the feature of the movement of the person. When the person is detected, the person detecting unit  304  gives a person ID to the detected person, and tracks the relevant person. The person detecting unit  304  further extracts a feature amount of the detected person (hereinafter referred to as a person feature amount). For example, the person feature amount includes a height, a face shape, and the like. Incidentally, the process of detecting the vehicle or the person from the video image is not limited to that described in the embodiment. 
     The identifying unit  305  identifies, from among the persons detected by the person detecting unit  304 , the person to be associated with the vehicle detected by the vehicle detecting unit  303 . In the present embodiment, the identifying unit  305  identifies the person who has gotten off the vehicle when the relevant vehicle was stopped in the parking lot  150  as the person to be associated with the vehicle. When the vehicle detected by the vehicle detecting unit  303  stops at the parking lot  150 , the registering unit  306  registers the stopped vehicle in the table storing unit  307  as a registration vehicle (or a registered vehicle). As just described, the vehicle which exists in the imaging area by the imaging device  110  is registered as the registration vehicle. Further, the person identified as the person to be associated with the registration vehicle is made correspondent (or is associated) with the registration vehicle, as an association person. The registering unit  306  registers the associated association person and registration vehicle in the table storing unit  307 . The determining unit  308  surveils the person detected by the person detecting unit  304  as the surveillance target, and determines whether or not the detected person is a suspicious person. 
       FIGS. 4A and 4B  are diagrams for describing an example of data configurations of tables stored in the table storing unit  307 . That is, the table storing unit  307  stores therein a registration vehicle table  400  of  FIG. 4A  and a detection person table  410  of  FIG. 4B . 
     The registration vehicle table  400  is used to store the information related to the registration vehicles. More specifically, the registration vehicle table  400  is used to store the vehicle ID of the registration vehicle, the vehicle position, the occupied area, the vehicle feature amount and the association person, while making them correspondent with others. The vehicle position is the position in the real space estimated from the position of the vehicle in the captured image. The occupied area is the information for identifying the area occupied by the vehicle based on the vehicle position. The vehicle feature amount is the vehicle feature amount extracted by the vehicle detecting unit  303  with respect to the registration vehicle. The association person is the person ID of the person identified as the person to be associated with the registration vehicle by the identifying unit  305 . 
     The detection person table  410  is used to store the information related to the person detected by the person detecting unit  304 . More specifically, the detection person table  410  is used to store the person ID and the person feature amount, while making them correspondent with each other. That is, the person feature amount of the association person is made correspondent with the registration vehicle by the person ID of the association person of the registration vehicle table  400  and the person ID of the detection person table  410 . 
     The surveillance system  100  first registers the registration vehicle and the association person in the table storing unit  307 , as a preprocess of detecting a suspicious person.  FIG. 5  is a flow chart for describing a data registering process in regard to the table storing unit  307 . The data registering process is a process of registering, when the vehicle included in the video image as the captured image stops, the stopped vehicle as the registration vehicle. When a plurality of vehicles are included in the video image, the surveillance system  100  performs the data registering process to each of the vehicle as the processing target. For example, when two vehicles A and B (not illustrated) are included in the video image, the surveillance system  100  parallelly performs the data registering process to each of the vehicles A and B as the processing targets. 
     In S 500 , the vehicle detecting unit  303  detects one vehicle to be processed from the video image as the captured image. Next, in S 501 , the vehicle detecting unit  303  gives the vehicle ID to the detected one vehicle, and starts tracking the detected vehicle. Next, in S 502 , the person detecting unit  304  detects a person from the video image. Next, in S 503 , the identifying unit  305  gives the person IDs to all the detected persons respectively, and starts tracking all the detected persons. 
     Next, in S 504 , the vehicle detecting unit  303  confirms whether or not the tracked vehicle stops. When the vehicle stops (YES in S 504 ), the vehicle detecting unit  303  advances the process to S 505 . On the other hand, when the vehicle does not stop (NO in S 504 ), the vehicle detecting unit  303  continues the tracking. In tracking of the vehicle, if it is necessary to perform the tracking up to an area outside the parking lot  150 , it is assumed that an area with a possibility of the tracking is included in the imaging range of the imaging device  110 . 
     In S 505 , the vehicle detecting unit  303  records the information of the registration vehicle in the registration vehicle table  400  with the vehicle as the registration vehicle. More specifically, the vehicle detecting unit  303  first identifies the stop position (vehicle position) and the occupied area of the vehicle. Further, the vehicle detecting unit  303  extracts the height of the vehicle, the shape of the vehicle shown in the captured image of the stopped vehicle, and the license plate information such as numbers, letters, symbols and the like shown on the license plate, as the vehicle feature amount. Then, the vehicle detecting unit  303  records (registers) the stop position, the occupied area and the image feature amount of the vehicle, in the registration vehicle table  400 . 
       FIGS. 6A and 6B  are explanatory diagrams for describing a process of identifying the stop position of the vehicle by the vehicle detecting unit  303 . Here, the vehicle detecting unit  303  obtains the position (x, y) in the plane of the parking lot  150 , as the stop position of the vehicle. The vehicle detecting unit  303  detects a tire  610  and a windshield  620  of a vehicle  600  on the video image illustrated in  FIG. 6A , by pattern matching. Then, the vehicle detecting unit  303  obtains the position of the lower part of the tire  610 . Since the tire  610  is in contact with the parking lot  150 , it is possible to calculate the distance between the position in the parking lot  150  and the imaging device  110 . The vehicle detecting unit  303  further obtains a traveling direction A of the vehicle  600  based on moving body analysis. 
     Further, as illustrated in  FIG. 6B , the vehicle detecting unit  303  detects the intersection between a line D connecting a corner C of the windshield  620  of the vehicle closer to the imaging device  110  and the traveling direction A and a line M extended vertically from the lower position of the tire  610 . Then, the vehicle detecting unit  303  obtains a height T of the vehicle  600  on the video image, based on the distance on the video image from the imaging device  110  to the detected intersection. Thus, it can be understood that the imaging magnification of the vehicle  500  is calculated from the distance from the lower position of the tire  610  in the video image to the imaging device  110 , and that the vehicle is captured at an angle θ (not illustrated) with respect to the imaging device  110 . Thus, it is possible to calculate an approximate value of the actual height of the vehicle. 
     The vehicle detecting unit  303  further calculates an approximate value of an occupied area E of the vehicle based on the traveling direction A of the vehicle  600 , and a front end F and a rear end B of the vehicle  600 . Thus, it is possible to calculate the position (X, y) of the vehicle  600  in the parking lot  150  from the occupied area E more accurately. There is a case where the vehicle  600  is hidden (or shadowed) by another vehicle. However, even in this case, it is possible to accurately identify the stop position and occupied area of the vehicle, by performing analysis from the traveling direction A of the vehicle  600 , the shape of the windshield, and the like. Besides, by installing some of the imaging devices  110  at an upper elevation of the parking lot  150 , the position of the moving body detected as the vehicle is directly reflected on the position on the video image. In this case, the vehicle detecting unit  303  may identify this position as the stop position of the vehicle. 
     Returning to  FIG. 5 , after the process of S 505 , the CPU  201  advances the process to S 506 . In S 506 , the vehicle detecting unit  303  confirms whether or not there is a state change of the vehicle (vehicle state change). Here, the state change is a change in the stop position of the vehicle, a change in the shape of the vehicle, or the like. The vehicle detecting unit  303  waits until the state change is confirmed (NO in S 506 ). Then, when the state change is confirmed (YES in S 506 ), the CPU advances the process to S 507 . In S 507 , the vehicle detecting unit  303  updates the information of the registration vehicle registered in the registration vehicle table  400 , according to the state change. For example, when the registration vehicle temporarily stops, moves again, and then stops again, the vehicle detecting unit  303  updates the vehicle position of the registration vehicle in the registration vehicle table  400 . 
       FIGS. 7A to 7C  are explanatory diagrams of the data registering process in S 506  and S 507 . After the entire shape of the registration vehicle  600  of  FIG. 6B  is registered as the shape of the registration vehicle in S 505 , as illustrated in  FIG. 7A , it is assumed that another (or the other) vehicle  700  stops at the position in front of the registration vehicle  600  as viewed from the imaging device  110 . In this case, a partial area  640  of the registration vehicle  600  is hidden by the other vehicle  700 . Therefore, the shape of the registration vehicle  600  becomes the shape of the area obtained by excluding the partial area  640  from the entire shape of the registration vehicle  600  illustrated in  FIG. 6B . In this case, the vehicle detecting unit  303  updates the shape of the registration vehicle  600  in the registration vehicle table  400 , that is, the entire area of the registration vehicle  600  is updated to the shape of the area obtained by excluding the area  640  from the entire area. 
     Thereafter, when the other vehicle  700  moves forward, the overlap between the other vehicle  700  and the registration vehicle  600  increases, so that the shape of the registration vehicle  600  further changes. As just described, every time the shape of the registration vehicle  600  changes, the vehicle detecting unit  303  updates the registered contents of the registration vehicle table  400  in S 507 . Incidentally, the vehicle detecting unit  303  can identify the partial area  640  in the registration vehicle  600  by detecting the partial area  640  as another moving body (the other vehicle  700 ). 
     When the plurality of vehicles are detected like this, the surveillance system  100  performs the data registering process for each of the detected vehicles as the processing target (registration vehicle). That is, the data registering process for the other vehicle  700  is also performed in parallel. Then, when the stop position of the other vehicle  700  is changed, the vehicle detecting unit  303  updates the vehicle position for the other vehicle  700 . 
     Further, as illustrated in  FIG. 7B , it is assumed that the person  710  exists at the position in front of the registration vehicle  600  as viewed from the imaging device  110  when the registration vehicle  600  stops. In this case, the shape of the registration vehicle  600  is the shape of the area obtained by excluding the area of a person  710  from the entire shape of the registration vehicle  600  illustrated in  FIG. 6B . Therefore, in S 504 , the vehicle detecting unit  303  registers the relevant shape in the registration vehicle table  400  as the shape of the registration vehicle  600 . Thereafter, when the person  710  moves and thus disappears from the front position of the registration vehicle  600  and the entire registration vehicle  600  is displayed on the video image, in S 507 , the vehicle detecting unit  303  updates the shape of the registration vehicle  600  to the entire shape thereof. At this time, the person detecting unit  304  detects the person  710  in response to an instruction from the vehicle detecting unit  303 , and registers the person ID and the like of the detected person in the detection person table  410 . 
     Besides, as illustrated in  FIG. 7C , there is a case where a person  720  detected outside the area of the registration vehicle  600  moves into the area of the registration vehicle  600  after the registration vehicle  600  stops. In this case, by referring to the tracking result, the vehicle detecting unit  303  updates the information of the registration vehicle, according to the change in the shape of the registration vehicle  600  caused by the movement of the person  720  from the outside of the registration vehicle  600 . 
     Incidentally, when identifying the shape of the vehicle from the video images obtained by the plurality of imaging devices  110 , it is preferable to perform the imaging from the respective positions at which the respective imaging devices  110  face each other. Thus, it is possible to grasp the states of the vehicle in the two directions, and it is thereby possible to improve accuracy in case of identifying the person who gets off the vehicle, as described later. 
     Returning to  FIG. 5 , after the process of S 507 , the CPU  201  advances the process to S 508 . In S 508 , the vehicle detecting unit  303  confirms whether or not the state change detected in S 505  is a specific state change. Here, the specific state change is a state change which results from the getting-off of the person from the vehicle. In the present embodiment, in correspondence with a fact that the door of the vehicle is opened before the person gets off, it is assumed that the shape change inside the area of the registration vehicle  600  is previously determined as the specific state change. When it is confirmed that the detected state change is the specific state change (YES in S 508 ), the vehicle detecting unit  303  advances the process to S 509 . On the other hand, when it is confirmed that the detected state change is not the specific state change (NO in S 508 ), the vehicle detecting unit  303  returns the process to S 506 . 
     In S 509 , the identifying unit  305  identifies, from among the persons detected by the person detecting unit  304 , the person who has gotten off the registration vehicle, that is, the person (association person) to be associated with the registration vehicle. When the detection position of the detected person satisfies the condition of being the position within the area where the registration vehicle is shown, the identifying unit  305  identifies the detected person as the person to be associated with the registration vehicle. This process is an example of an identifying process of identifying the person to be associated with the registration vehicle, based on a predetermined condition. Incidentally, the identifying unit  305  may identify a plurality of persons as the persons to be associated with one registration vehicle. When identifying the person to be associated with the registration vehicle (YES in S 509 ), the identifying unit  305  advances the process to S 510 . On the other hand, when failing to identify the person to be associated with the registration vehicle (NO in S 509 ), the identifying unit  305  returns the process to S 506 . 
       FIGS. 8A and 8B  are explanatory diagrams of the data registering process in S 508  and S 509 . As illustrated in  FIG. 8A , it is assumed that a door  650  of the registration vehicle  600  is opened. In this case, in S 508 , the vehicle detecting unit  303  determines that the specific state change occurs, and the identifying unit  305  attempts to identify the person who gets off the registration vehicle, that is, the person to be associated with the registration vehicle. In the example illustrated in  FIG. 8A , since the detection position of a person  800  is the position within the area where the registration vehicle  600  is shown, the identifying unit  305  identifies the person  800  as the person to be associated with the registration vehicle  600 . This process is an example of a process of identifying the person detected in the area overlapping the area where the vehicle is detected, as the person to be associated with the registration vehicle. Incidentally, it is assumed that the change in the shape of the registration vehicle as described with reference to  FIGS. 7A to 7C  does not correspond to the specific state change. 
     As illustrated in  FIG. 8B , there is a case where another vehicle  810  overlaps the registration vehicle  600 . Even in this case, the vehicle which causes the change in the shape due to the opening of the door is only the registration vehicle  600 . Therefore, the identifying unit  305  can identify the person  800  as the person who gets off the registration vehicle  600 , not the vehicle  810 . Namely, the identifying unit  305  can identify the person  800  as the person associated only with the registered vehicle  600 . As just described, since the identifying unit  305  considers the change in the shape, it is possible to accurately identify which of the other vehicle  810  and the registration vehicle  600  the person  800  should be associate with. Incidentally, when the degree of overlap is lower than a threshold value, it may be possible to determine that the registration vehicle does not overlap another vehicle. 
     Returning to  FIG. 5 , in S 510 , the person detecting unit  304  extracts the person feature amount of the person identified as the person to be associated. For example, the person detecting unit  304  extracts the height of the person or the face of the person as the image feature amount. Here, the method of identifying the height is similar to the method of identifying the height of the vehicle as described with reference to  FIG. 6B . Incidentally, it is possible to use a face authentication technique or the like in regard to the face of the person. Besides, if the face or the height cannot be identified because the person faces down or bends down, the person detecting unit periodically attempts to extract the feature until the feature can be extracted, by tracking the person associated. In a case where the person feature amount is extracted from the video images from the multiple directions of the plurality of imaging devices, it is possible to shorten the tracking period. 
     Then, the registering unit  306  records (registers) the person ID of the person to be associated and the person feature amount while making them correspondent with each other, in the detection person table  410 . The registering unit  306  further records (registers) the person ID of the person identified as the person to be associated in the column of the association person, while making it correspondent with the vehicle ID of the registration vehicle in the registration vehicle table  400 . Thus, the data registering process is completed. In a case where the plurality of persons to be associated are identified, the person ID of each of the plurality of identified persons is registered in the column of the association person. Also, there is a case where one person is identified as the person to be associated with the plurality of registration vehicles. In this case, the person ID of the same person is registered as the person ID of the association person, while making it correspondent with each of the plurality of registration vehicles. 
     Incidentally, the process of identifying the person to be associated is not limited to that described in the embodiment. As another example, the identifying unit  305  may identify the person detected in the vehicle area based on the position of the registration vehicle as the person to be associated with the registration vehicle. In the present embodiment, it is assumed that the vehicle area is equivalent to a predetermined distance range from the occupied area of the registration vehicle. In this case, for example, in an example of  FIG. 9A , a person  900  detected in the vehicle area is identified as the person to be associated with the registration vehicle  600 . In an example of  FIG. 9B , a person  920  detected at the position overlapping the vehicle areas of both the registration vehicle  600  and the other vehicle  910  is registered as the person to be associated with both the registration vehicle  600  and the other vehicle  910 . 
     In the example of  FIG. 9A , it is conceivable that the person  900  moves from the outside of the vehicle area of the registration vehicle  600  to the inside of the vehicle area, and further moves to the outside of the vehicle area. Correspondingly, when the person  900  is detected in the vehicle area, the identifying unit  305  confirms the before-identification movement trajectory of the person  900  which is detected by the person detecting unit  304 . Then, when the person  900  is the person who has moved from outside the vehicle area, the identifying unit  305  does not identify the person  900  as a person to be associated with the registration vehicle  600 . Thus, it is possible to prevent an erroneous determination that a person who is moving in the vehicle area of the registration vehicle  600  is a person who has gotten off the registration vehicle  600 . 
     Further, as illustrated in  FIG. 9C , there is a case where a person  930  moves from the outside of the vehicle area and once disappears from the video image because of hidden by the registration vehicle  600 , and the same person  930  again appears in the vehicle area. In this case, the person detecting unit  304  estimates that the person once disappearing and the person again appearing are the same person on the basis of the face authentication or the like, and thus does not give a new person ID. Then, the identifying unit  305  does not identify the person  930  as a person to be associated with the registration vehicle  600 . 
     As another example, as illustrated in  FIG. 10 , it is assumed that, in a case where a person  1000  is detected from the video image captured by the imaging device  110  placed above, the moving object  1000  which is separated from the registration vehicle  600  is detected. In this case, the moving object  1000  may be identified as a person to be associated. As just described, the method of identifying the person to be associated may be determined according to the layout or disposition of the imaging device  110  in the parking lot  150 . In addition, when the process of identifying a person to be associated is performed from the video images of the plurality of imaging devices  110  in different imaging directions, it is possible to more accurately identify the relevant person (the person to be associated). 
       FIG. 11  is a flow chart for describing a registration data erasing process. The registration data erasing process is a process for erasing the data related to the registration vehicle from the table storing unit  307 , when the registration vehicle moves outside the surveillance area. In a case where a plurality of registration vehicles are registered in the registration vehicle table  400 , the surveillance system  100  parallelly performs the registration data erasing processes for each of the registration vehicles registered in the registration vehicle table  400  as the processing target. 
     In S 1100 , the vehicle detecting unit  303  surveilles or monitors whether or not the movement of the registration vehicle registered in the registration vehicle table  400  is detected. The vehicle detecting unit  303  waits until the movement is detected (NO in S 1100 ), and, when the movement is detected (YES in S 1100 ), advances the process to S 1101 . In S 1101 , the vehicle detecting unit  303  confirms the vehicle position after the movement. When the vehicle position after the movement is within the surveillance area (YES in S 1101 ), the vehicle detecting unit  303  advances the process to S 1102 . On the other hand, when the vehicle position after the movement is outside the surveillance area (the parking lot  150 ) (NO in S 1101 ), the vehicle detecting unit  303  advances the process to S 1104 . 
     In S 1102 , the vehicle detecting unit  303  confirms whether or not the registration vehicle stops. When the registration vehicle stops (YES in S 1102 ), the vehicle detecting unit  303  advances the process to S 1103 . On the other hand, when the registration vehicle does not stop, that is, when the vehicle is moving (NO in S 1102 ), the vehicle detecting unit  303  returns the process to S 1101  to continue the tracking. In S 1103 , the registering unit  306  updates the information of the registration vehicle. More specifically, the registering unit  306  updates the information related to the vehicle position, the shape and the like of the registration vehicle in the registration vehicle table  400 , in accordance with the state of the registration vehicle after the stop. The registering unit  306  then returns the process to S 1100 . On the other hand, in S 1104 , the vehicle detecting unit  303  erases the information related to the registration vehicle to be processed, from the registration vehicle table  400 . Thus, the registration data erasing process ends. 
     As described above, by performing the data registering process and the registration data erasing process, the surveillance system  100  can register the vehicle stopped within the surveillance area and the person associated with the relevant vehicle while making them correspondent with each other. 
       FIG. 12  is a flow chart for describing a suspicious person detecting process. The suspicious person detection process is a process of detecting whether or not the person detected in the surveillance area is a suspicious person. In a case where a plurality of persons are included in the video image, the surveillance system  100  performs the suspicious person detecting process for each of the persons as the processing target. In S 1200 , the person detecting unit  304  detects a person. The surveillance system  100  performs the subsequent processes to the person detected in S 1200 , as the determination target. Hereinafter, the person to be detected as the determination target is referred to as a target person. Next, in S 1201 , the person detecting unit  304  gives an ID to the target person, and starts tracking the target person. Next, in S 1202 , the person detecting unit  304  extracts the person feature amount of the target person. As described above, the person feature amount to be extracted here is the face, the height and the like of the target person. 
     Next, in S 1203 , the determining unit  308  determines whether or not the position of the target person is within a detection area. Here, the detection area is an area which is based on the position of the registration vehicle.  FIG. 13  is a diagram for describing an example of the detection area. In the present embodiment, it is assumed that the range from an occupied area  1300  of the registration vehicle to the position one meter away is a detection area  1310 . Incidentally, in the case where the plurality of registration vehicles are registered in the registration vehicle table  400 , the detection area is defined for each of the registration vehicles. In a case where the position of the target person is within the detection area of any of the registration vehicles, the determining unit  308  determines that the position of the target person is within the detection area. Incidentally, the detection area may be an area range with based on the position of the registration vehicle, that is, the detection area is not limited to that described the embodiment. 
     When the position of the target person is within the detection area (YES in S 1203 ), the determining unit  308  advances the process to S 1204 . On the other hand, when the position of the target person is not within the detection area (NO in S 1203 ), the determining unit  308  again performs the process of S 1203 . In S 1204 , the determining unit  308  refers to the registration vehicle table  400 , and identifies, based on the correspondence relation between the registration vehicle and the association person, the person associated with the registration vehicle corresponding to the detection area determined to include the position of the target person. As another example, the determining unit  308  may identify the association person from a recognition result of the license plate of the vehicle which is stopping within the detection area determined to include the target person. 
     Next, in S 1205 , the determining unit  308  compares the person feature amount of the association person identified in S 1204  with the person feature amount of the target person extracted in S 1202 , thereby determining whether or not the target person is the association person. For example, when a degree of similarity of the person feature amount of the association person and the person feature amount of the target person is equal to or higher than a threshold value, the determining unit  308  determines that the target person is the association person. When it is determined that the target person is the association person (YES in S 1205 ), the determining unit  308  returns the process to S 1203 . On the other hand, when it is determined that the target person is not the association person (NO in S 1205 ), the determining unit  308  advances the process to S 1206 . 
     In S 1206 , the determining unit  308  controls the communication processing unit  301  to transmit notification information instructing to output (or issue) a warning to the terminal device  130 . This process is an example of an output controlling process for controlling to output the warning. When receiving the notification information, the terminal device  130  performs a process of displaying the warning on the displaying unit, outputting a warning sound, or the like. On the other hand, when it is determined that the target person is the association person, the terminal device does not output the warning. That is, when the person who got off the vehicle comes back to the vehicle, the relevant person is not detected as a suspicious person, so that no useless warning is performed. 
       FIGS. 14A and 14B  are explanatory diagrams of the suspicious person detecting process.  FIG. 14A  is the diagram showing a case where two registration vehicles  1400  and  1410  are stopping. Detection areas  1401  and  1411  are defined for the registration vehicles  1400  and  1410 , respectively. In this state, it is assumed that a person  1420  is detected to be located at a position A. The position A is not included in any of the detection areas  1401  and  1411 . That is, there is no person who is approaching any of the registration vehicles  1400  and  1410 . In this case, NO is given in S 1203 , and the tracking of the person is continued. 
     On the other hand, it is assumed that the person  1420  is detected to be located at a position B. The position B is within the detection area  1401 . Therefore, it is determined whether or not the person  1420  is the association person of the registration vehicle  1400  corresponding to the detection area  1401  (S 1204 , S 1205 ). In a case where the person  1420  is the association person of the registration vehicle  1400 , a person estimated to have gotten off the registration vehicle  1400  approaches the vehicle. In this case, the determining unit  308  performs control so as to continue the tracking without performing the process of outputting a warning. On the other hand, in a case where the person  1420  is not the association person of the registration vehicle  1400 , a person other than the person estimated to have gotten off the registration vehicle  1400  approaches the vehicle. In this case, the determining unit  308  performs the process of outputting a warning. 
       FIG. 14B  is the diagram showing a case where two registration vehicles  1430  and  1440  are stopping and detection areas  1431  and  1441  of the respective registration vehicles  1430  and  1440  overlap each other. In such a state, it is assumed that a person  1450  is detected to be located in the overlapping area of the detection areas  1431  and  1441 . In this case, in S 1204 , the determining unit  308  identifies the association person corresponding to the detection area  1431 , and further identifies the association person corresponding to the detection area  1441 . Then, in S 1205 , the determining unit  308  determines whether or not the person  1450  is the association person of the registration vehicle  1430  corresponding to the detection area  1431 , and further determines whether or not the person  1450  is the association person of the registration vehicle  1440  corresponding to the detection area  1441 . 
     Then, it is assumed that the person  1450  is determined to be the association person of the registration vehicle  1430  and is determined not to be the association person of the registration vehicle  1440 . In this case, a person estimated to have gotten off the registration vehicle  1430  approaches the registration vehicle  1430 , and a person other than the person estimated to have gotten off the registration vehicle  1440  approaches the registration vehicle  1440 . 
     Besides, it is assumed that the person  1450  is determined to be the association persons of both the registration vehicles  1430  and  1440 . In this case, a person who may have gotten off the registration vehicle  1430  approaches the registration vehicle  1430 , and a person who may have gotten off the registration vehicle  1440  approaches the registration vehicle  1440  as well. 
     As just described, when the person is determined to be the association person made correspondent to the two registration vehicles, it is unknown to which of the vehicles the relevant person corresponds. Therefore, in this case, the determination unit  308  estimates a probability of being the person who has gotten off the registration vehicle to be low as compared with the case where the person is determined to be the association person made correspondent to one registration vehicle. Thus, it is possible to determine a suspicious person more accurately. 
     As described above, in the surveillance area where unspecified persons are to be surveilled, it is possible for the surveillance system  100  according to the present embodiment to facilitate the determination as to whether or not the person is the suspicious person, without previously registering the feature and the like of the person to be surveilled. 
     A first modified example of the surveillance system  100  according to the present embodiment will be described. In the present embodiment, the process of outputting the warning is performed when the person other than the association person is located in the detection area. However, in place of this, the process of outputting the warning may be performed only when the person other than the association person continuously stays in the detection area for a certain time. Thus, it is possible to avoid erroneously detecting a person who merely passes through the detection area as the suspicious person. 
     Also, as a second modified example, the determining unit  308  may control to transmit all of determination results as the notification information to the terminal device  130 . In other words, the determining unit  308  may control to transmit information corresponding to the determination result to the terminal device  130 . For example, the determining unit  308  may control to transmit information related to a probability that a person is the suspicious person. As just described, the output timing to the terminal device  130  and the kind of output information are not limited to those described in the embodiment. 
     Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the above specific embodiments. Namely, various modifications and changes are possible within the scope of the substance of the present invention described in the claims. 
     OTHER EMBODIMENTS 
     It is possible to achieve the present invention also by supplying a program for realizing one or more of the functions of the above embodiment to a system or an apparatus via a network or a storage medium and causing one or more processors in the computer of the system or the apparatus to read and execute the supplied program. Also, it is possible to achieve the present invention by a circuit (e.g., ASIC) for realizing one or more functions of the above embodiment. 
     According to each of the embodiments described above, it is possible to facilitate the determination as to whether or not a person is a suspicious person. 
     Embodiment(s) of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s). The computer may comprise one or more processors (e.g., central processing unit (CPU), micro processing unit (MPU)) and may include a network of separate computers or separate processors to read out and execute the computer executable instructions. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, one or more of a hard disk, a random-access memory (RAM), a read only memory (ROM), a storage of distributed computing systems, an optical disk (such as a compact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™), a flash memory device, a memory card, and the like. 
     While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions. 
     This application claims the benefit of Japanese Patent Application No. 2016-116222, filed Jun. 10, 2016, which is hereby incorporated by reference herein in its entirety.