Patent Publication Number: US-2022222961-A1

Title: Attribute determination device, attribute determination program, and attribute determination method

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation application of International Application Number PCT/2019/038734 filed on Oct. 1, 2019 and designated the U.S., the entire contents of which are incorporated herein by reference. 
    
    
     FIELD 
     The present invention relates to an attribute determination device, an attribute determination program, and an attribute determination method. 
     BACKGROUND 
     There is an image analysis technique for identifying that a person appears in an image such as a still image and a moving image. The image analysis technique may determine (estimate) not only the presence or absence of a person but also attributes of the identified person, for example, gender and age. 
     Information about the determined attributes may be used, for example, for marketing or personal authentication. For example, in a facility where people gather, such as a shopping mall, cameras are installed in various places to determine the attributes of each visitor. Then, the determined attributes are reflected in future services and marketing for visitors to help improve the quality of the services and marketing. 
     Techniques related to image analysis are described in the following Patent Literature 1 to Patent Literature 3. 
     CITATION LIST 
     Patent Literature 
     
         
         Patent Literature 1: Japanese Patent Application Publication No. 2013-242825 
         Patent Literature 2: Japanese Patent Application Publication No. 2013-58060 
         Patent Literature 3: Japanese Patent Application Publication No. 2012-190159 
       
    
     SUMMARY 
     An attribute determination device includes a memory and a processor coupled to the memory and the processor is configured to acquire images captured by a plurality of image capturing devices configured to capture the images, extract an image in which a first object which is an object whose attribute is to be determined appears, analyze the image of the first object appearing in the extracted image, calculate a first probability that the first object has a first attribute for each of a first image capturing devices that have captured the image in which the first object appears, and determine whether the first object has the first attribute based on a second probability and the first probability, the second probability being a probability for each of the plurality of image capturing devices and indicating a probability that an object having the first attribute appears in a captured image. 
     The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims. 
     It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating a configuration example of an attribute determination system  10 . 
         FIG. 2  is a diagram illustrating a configuration example of the attribute determination device  200 . 
         FIG. 3  is a diagram illustrating a configuration example of the image capturing device  100 . 
         FIG. 4  is a diagram illustrating an example of a functional block of the attribute determination system  10 . 
         FIG. 5  illustrates an example of the attribute determination result table  226 . 
         FIG. 6  illustrates an example of the attribute score table  225 . 
         FIG. 7  illustrates an example of a processing flowchart of the attribute determination processing S 100 . 
         FIG. 8  illustrates an example of a processing flowchart of the person detection processing S 200 . 
         FIG. 9  illustrates an example of a processing flowchart of the image analysis processing S 300 . 
         FIG. 10  illustrates an example of a processing flowchart of the final attribute determination processing S 400 . 
         FIG. 11  illustrates an example of a processing flowchart of the attribute score update processing S 500 . 
         FIG. 12  illustrates an example of the attribute determination result table  226  after the update. 
         FIG. 13  illustrates an example of the attribute score table  225  after the update. 
         FIG. 14  illustrates an example of the attribute determination result table  226  in the case of determining a plurality of types of attributes. 
         FIG. 15  illustrates an example of the attribute score table  225  in the case of determining a plurality of types of attributes. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     In the determination of the attributes, for example, when the sharpness of the face of a person appearing in an image is low, the accuracy of the determination may be reduced, for example, resulting in an erroneous determination or no determination. Further, even when the image of the person is clear, the accuracy of the determination of the attributes may be reduced depending on the content of the attributes to be determined and the physical features of the person. 
     The reduced accuracy of the determination of the attributes may cause the improvement in quality to not be achieved even when the attributes are reflected in services and marketing. 
     Therefore, one disclosure provides an attribute determination device, an attribute determination program, and an attribute determination method for improving the accuracy of determination for an object whose attribute is to be determined appearing in an image. 
     First Embodiment 
     A first embodiment will be described. 
     &lt;Configuration Example of Attribute Determination System&gt; 
       FIG. 1  is a diagram illustrating a configuration example of an attribute determination system  10 . The attribute determination system  10  is a communication system that includes image capturing devices  100 - 1  to  100 - 4 , an attribute determination device  200 , and a network NW1. The attribute determination system  10  is, for example, a system that determines (estimates) an attribute of a person appearing in an image or images captured by the image capturing devices  100 - 1  to  100 - 4 . The attribute determination system  10  is installed in a facility such as a shopping center or a department store (hereinafter, may be referred to as an installation facility). 
     The image capturing devices  100 - 1  to  100 - 4  (hereinafter, each of which may be referred to as an image capturing device  100 ) are each a device for capturing an image of a capture range of the device, and are each a camera installed for crime prevention, surveillance, or image acquisition. The image capturing device  100 , for example, captures a moving image for a predetermined time. Further, the image capturing device  100  may capture still images at predetermined intervals. Further, the image capturing device  100  may capture both a still image and a moving image. Note that the attribute determination system  10  in  FIG. 1  includes four image capturing devices  100 , but any number of image capturing devices  100  can be included as long as it is two or more. 
     The image capturing devices  100  are installed so as to capture images of various places in the installation facility. The image capturing device  100  is installed in each sales section, for example, to capture the entire sales section. For example, the image capturing device  100 - 1  is installed in an outdoor equipment section, the image capturing device  100 - 2  is installed in a women&#39;s clothing section, the image capturing device  100 - 3  is installed in a men&#39;s clothing section, and the image capturing device  100 - 4  is installed in a food section. 
     The image capturing device  100  captures an image, and then transmits the captured image to the attribute determination device  200  (S 1  to S 4 ). Further, the image capturing device  100  may store the captured image in a memory or a hard disk, and periodically or in response to a request from the attribute determination device  200 , may transmit the stored image to the attribute determination device  200  (S 1  to S 4 ). 
     The attribute determination device  200  is, for example, a server machine or a computer, which performs attribute determination processing for analyzing an image captured by the image capturing device  100  and determining an attribute of a visitor. When the attribute determination device  200  detects that a predetermined time has come, for example, the attribute determination device  200  performs the attribute determination processing. Further, the attribute determination device  200  acquires (receives) the image captured by each image capturing device  100  (S 5 ). For example, in the attribute determination processing, the attribute determination device  200  requests the image capturing device  100  to transmit an image. Alternatively, the attribute determination device  200  may store in a memory or a hard disk the image transmitted from the image capturing device  100  at any timing. 
     The network NW1 is a network through which communication is made between the attribute determination device  200  and the image capturing device  100 , and is, for example, a local network or an intranet in a facility in which the attribute determination system  10  is installed, or the Internet. The attribute determination device  200  and the image capturing device  100  communicate with each other via the network NW1 to transmit and receive captured images. The network NW1 is a network for providing wired or wireless connection. 
     &lt;Configuration Example of Attribute Determination Device&gt; 
       FIG. 2  is a diagram illustrating a configuration example of the attribute determination device  200 . The attribute determination device  200  is a device that can communicate with other devices via the network NW1, and is, for example, a computer or a server machine. 
     The attribute determination device  200  includes a CPU (Central Processing Unit)  210 , a storage  220 , a memory  230 , a NIC (Network Internet Card)  240 , and a display  250 . 
     The storage  220  is an auxiliary storage device such as a flash memory, an HDD (Hard Disk Drive), or an SSD (Solid State Drive), which stores programs and data. The storage  220  stores an image acquisition program  221 , an attribute determination program  222 , an attribute score update program  223 , an image data table  224 , an attribute score table  225 , and an attribute determination result table  226 . Note that the image data table  224 , the attribute score table  225 , and the attribute determination result table  226  may be stored in the memory  230 . 
     The memory  230  is an area in which a program stored in the storage  220  is loaded. The memory  230  may be also used as an area in which the program stores data. 
     The NIC  240  is an interface for connecting to the network NW1. The NIC  240  is, for example, an interface device having a port connected to the Internet, such as a network interface card. 
     The display  250  is a display unit that displays an image captured by the image capturing device  100 , an attribute determination result, and the like. 
     The display  250  may be integrated with the attribute determination device  200 , or may be a device connected by a cable or the like. 
     The CPU  210  is a processor that loads a program stored in the storage  220  into the memory  230 , and executes the loaded program to construct corresponding units and to implement steps of processing. 
     The CPU  210  executes the image acquisition program  221  so as to construct an image acquisition unit to perform image acquisition processing. The image acquisition processing is processing of acquiring an image captured by the image capturing device  100  from the image capturing device  100 . The attribute determination device  200  receives image data from the image capturing device  100  via, for example, the NIC  240  to acquire an image therefrom. 
     The CPU  210  executes the attribute determination program  222  so as to construct an image analysis unit and an attribute determination unit to perform attribute determination processing. The attribute determination processing is processing of determining attributes of people appearing in the image captured by the image capturing device  100  for each person. In the attribute determination processing, the attribute determination device  200  finally determines an attribute of the person based on an attribute determination resulting from image analysis and the characteristics of the image capturing device  100 . 
     Further, the CPU  210  executes a person detection module  2221  included in the attribute determination program  222  so as to construct a person detection unit and an image analysis unit to perform person detection processing. The person detection processing is processing of detecting a new person from the image captured by the image capturing device  100 . The new person is, for example, a person whose attribute has not yet been determined in a series of steps of image analysis processing. 
     Further, the CPU  210  executes an image analysis module  2222  included in the attribute determination program  222  so as to construct an image analysis unit to perform image analysis processing. The image analysis processing is processing of detecting (extracting) an image in which a person appears for each image capturing device  100 , performing image analysis for each of the detected image capturing devices  100 , and calculating the probability that the person has a certain attribute. 
     Further, the CPU  210  executes a final attribute determination module  2223  included in the attribute determination program  222  so as to construct an attribute determination unit to perform final attribute determination processing. The final attribute determination processing is processing of finally determining the attribute of the person based on the probability obtained from the image analysis and the characteristics of the image capturing device  100  that has captured the image. 
     The CPU  210  executes the attribute score update program  223  so as to construct an attribute score update unit to perform attribute score update processing. The attribute score update processing is processing of updating the attribute score according to the attribute determination result. 
     &lt;Configuration Example of Image Capturing Device&gt; 
       FIG. 3  is a diagram illustrating a configuration example of the image capturing device  100 . The image capturing device  100  is a device that captures an image (still image, moving image, or both) of a predetermined range, and is, for example, a camera or a device including a camera. 
     The image capturing device  100  includes a CPU  110 , a storage  120 , a memory  130 , a NIC  140 , and a camera  160 . 
     The storage  120  is an auxiliary storage device such as a flash memory, an HDD, or an SSD that stores programs and data. The storage  120  stores an image capturing program  121  and an image transmission program  122 . 
     The memory  130  is an area in which a program stored in the storage  120  is loaded. The memory  130  may be also used as an area in which the program stores data. 
     The NIC  140  is an interface for connecting to the network NW1. The NIC  140  is, for example, an interface device having a port connected to the Internet, such as a network interface card. 
     The camera  160  is a device that captures an image (moving image, still image, etc.) of a predetermined range. The camera  160  captures images regularly or irregularly. Further, the camera  160  is triggered to capture an image by, for example, the CPU  110 . Further, the image captured by the camera  160  is stored in, for example, the memory  130  or the storage  120 . 
     The CPU  110  is a processor that loads a program stored in the storage  120  into the memory  130 , and executes the loaded program to construct corresponding units and to implement steps of processing. 
     The CPU  110  executes the image capturing program  121  to perform image capturing processing. The image capturing processing is processing of capturing an image of the capture range of the image capturing device  100 , for example, regularly. The captured image is, for example, stored in an external or internal memory or a hard disk, or transmitted to the attribute determination device  200 . 
     The CPU  110  executes the image transmission program  122  to perform image transmission processing. The image transmission processing is processing of transmitting an image captured by the image capturing device  100  to the attribute determination device  200 , for example, regularly. 
     &lt;Configuration Example of Functional Block of Attribute Determination System&gt; 
       FIG. 4  is a diagram illustrating an example of a functional block of the attribute determination system  10 . The image capturing device  100  includes an image capturing unit  1001  and an image transmission unit  1002 . The image capturing unit  1001  and the image transmission unit  1002  are constructed, for example, by the processor of the image capturing device  100  executing a program. Further, the image capturing unit  1001  may be a camera. Further, the image transmission unit  1002  may be a device for a communication interface such as a network interface card. 
     The attribute determination device  200  includes an image acquisition unit (acquirer)  2001 , an image analysis unit  2002 , an attribute determination unit  2003 , and an attribute score update unit  2004 . The image acquisition unit  2001 , the image analysis unit  2002 , the attribute determination unit  2003 , and the attribute score update unit  2004  are constructed, for example, by the processor of the attribute determination device  200  executing a program. Further, the image acquisition unit  2001  may be a device for a communication interface such as a network interface card. 
     The image capturing unit  1001  captures an image of a predetermined range regularly or irregularly. The image capturing unit  1001  stores the captured image in an internal or external memory or a hard disk, or passes the captured image to the image transmission unit  1002 . 
     The image transmission unit  1002  transmits the image to the attribute determination device  200 . The image transmission unit  1002  transmits an image, for example, regularly or irregularly, or transmits the image in response to a request from the attribute determination device  200 . The image transmission unit  1002  is connected to the attribute determination device  200  by wire or wirelessly so as to communicate with the attribute determination device  200 . 
     The image acquisition unit  2001  acquires the image from the image capturing device  100 . The image acquisition unit  2001  receives the image transmitted by the image transmission unit  1002 , stores the image in the image data table  224 , or passes the image to the image analysis unit  2002 . The image acquisition unit  2001  is connected to the image capturing device  100  by wire or wirelessly so as to communicate with the image capturing device  100 . 
     The image analysis unit  2002  identifies a person appearing in the acquired image, analyzes the identified person, and calculates the probability that the identified person has a certain attribute (first attribute). The image analysis unit  2002  also searches for images captured by other image capturing devices  100  in which the identified person appears. Then, the image analysis unit  2002  calculates the probability that the identified person has the certain attribute (first attribute) for each image capturing device  100  that has captured the identified person. 
     The attribute determination unit  2003  finally determines whether or not the identified person has the certain attribute based on the probability calculated by the image analysis unit  2002  and the probability for each image capturing device  100  which is a probability that a person having the certain attribute appears in the image captured by the image capturing device  100 . A series of steps of processing executed by the image analysis unit  2002  and the attribute determination unit  2003  is referred to as attribute determination processing. 
     Note that the attribute determination processing is executed, for example, when a predetermined time is reached, when a predetermined amount of images are accumulated, or when a request is made from an administrator of the attribute determination system  10 . 
     The attribute score update unit  2004  updates the attribute score of each image capturing device  100  in the attribute score table  225  according to the determination result from the attribute determination unit  2003 . The attribute score is, for example, a probability indicating how many people in a captured image have the certain attribute. The attribute score may be a numerical value based on statistics, or may be a numerical value based on the characteristics of the capture location (e.g., the location is where there are many men). Further, the attribute score may be a statistical value such as, for example, the total number of people appearing in the captured image or the number of people having the certain attribute. 
     &lt;Various Tables&gt; 
     The tables stored in the attribute determination device  200  will be described. 
     &lt;1. Image Data Table&gt; 
     The image data table  224  is a table for storing images acquired from the image capturing devices  100 . The attribute determination device  200  stores the image for each image capturing device  100 . Further, after performing the attribute determination processing, for example, the attribute determination device  200  may delete the corresponding image stored in the image data table  224 . 
     &lt;2. Attribute Determination Result Table&gt; 
       FIG. 5  illustrates an example of the attribute determination result table  226 . The attribute determination result table  226  is a table for storing the attribute determination result of the attribute determination processing. 
     In the attribute determination result table  226 , for example, “person” and “first attribute (male)” are stored. 
     The “person” is identification information indicating a person to be subject to attribute determination, and is, for example, the name of the person, the number for identifying the person, the time of image capturing, the identifier of the image capturing device, or the like. In  FIG. 5 , for example, the person identified by one letter of the alphabet such as “X” may be referred to as Person X or the like. 
     The “first attribute (male)” is information indicating whether or not the person has that attribute (first attribute). In  FIG. 5 , the first attribute indicates whether or not the person is male, a circle indicates that the person is male, and a cross indicates that the person is not male. Note that a plurality of types of attributes may be stored in the attribute determination result table  226 , for example, each indicated as the nth attribute (n is an integer). When the attributes to be stored in the attribute determination result table  226  are stored for a plurality of types of attributes, information indicating whether or not the person has the attribute is stored for each type of attribute. 
     &lt;3. Attribute Score Table&gt; 
       FIG. 6  illustrates an example of the attribute score table  225 . The attribute score table  225  is a table for storing the attribute score for each image capturing device  100 . 
     In the attribute score table  225 , for example, “total number”, “first attribute (male)”, and “first attribute score” are stored. 
     The “total number” is information indicating the total number of people appearing in the image captured by the corresponding image capturing device  100 . The “total number” is updated after a new person is identified in the processing described below. 
     The “first attribute (male)” is information indicating the total number of people having that attribute (first attribute) among the people appearing in the image captured by the corresponding image capturing device  100 . The “first attribute (male)” is updated after it is determined that a certain person has that attribute in the processing described below. Note that the attribute score table  225  in  FIG. 6  stores only the attribute score related to the first attribute, but may store a plurality of types of attributes. In that case, the attribute score table  225  can store the total number of people having that attribute, as in the case of the “first attribute (male)”. 
     The “first attribute score” is information indicating the probability that the person appearing in the image captured by the corresponding image capturing device  100  is a person having that attribute (first attribute). The “first attribute score” is, for example, a numerical value obtained by dividing the numerical value of the “first attribute (male)” by the numerical value of the “total number”. Note that, in the case where the “first attribute score” is a numerical value obtained by dividing the numerical value of the “first attribute (male)” by the numerical value of the “total number”, the “first attribute score” does not have to be stored in the attribute score table  225  because it can be calculated from the information elements of the “total number” and the “first attribute (male)”. 
     &lt;Attribute Determination Processing&gt; 
     The attribute determination processing S 100  executed by the attribute determination device  200  will be described.  FIG. 7  illustrates an example of a processing flowchart of the attribute determination processing S 100 . 
     When the attribute determination device  200  detects a trigger to execute the attribute determination processing, the attribute determination device  200  executes the attribute determination processing S 100 . The attribute determination device  200  executes the person detection processing in the attribute determination processing S 100  (S 200 ). 
     The person detection processing S 200  is processing of detecting a new person (a person who has not been detected in the person detection processing S 200  performed in the past in the series of steps of processing) from the image. The details of the person detection processing S 200  will be described later. 
     When the attribute determination device  200  does not detect a new person in the person detection processing S 200  (No in S 100 - 1 ), the attribute determination device  200  ends the attribute determination processing S 100 . 
     On the other hand, when the attribute determination device  200  detects a new person in the person detection processing S 200  (Yes in S 100 - 1 ), the attribute determination device  200  executes the image analysis processing (S 300 ). The image analysis processing S 300  is processing of determining an attribute (provisional attribute) of the person based on the image of the detected person. The details of the image analysis processing S 300  will be described later. 
     The attribute determination device  200  executes the final attribute determination processing after executing the image analysis processing S 300  (S 400 ). The final attribute determination process S 400  is processing of determining the attribute (final attribute) of the detected person based on the provisional probability (first probability) calculated in the image analysis processing S 300  and the attribute score (second probability) for each image capturing device  100 . The details of the final attribute determination processing S 400  will be described later. 
     The attribute determination device  200  executes the attribute score update processing after executing the attribute determination final processing S 400  (S 500 ). The attribute score update processing S 500  is processing of reflecting the attribute of the person determined in the final attribute determination processing S 400  in the attribute score for each image capturing device  100  and updating the attribute score table  225 . The details of the attribute score update processing S 500  will be described later. 
     The attribute determination device  200  executes the person detection processing S 200  again after executing the attribute score update processing S 500 . Then, the attribute determination device  200  repeats the series of steps of processing in the attribute determination processing S 100  until a new person is not detected in the person detection processing S 200  (until the attribute determination for all people appearing in the image is completed). 
     &lt;Person Detection Processing&gt; 
       FIG. 8  illustrates an example of a processing flowchart of the person detection processing S 200 . The attribute determination device  200  selects an image in the person detection processing S 200  (S 200 - 1 ). As the image to be selected, for example, an image captured by the image capturing device  100  installed near the entrance of the facility is selected. Further, the image to be selected may be randomly selected. 
     The attribute determination device  200  searches for a new person (first object) from a plurality of persons (objects whose attribute is to be determined) appearing in the selected image (S 200 - 2 ). The new person is a person who has not been detected in the person detection processing S 200 . Further, the new person refers to a person who has not been subjected to the attribute determination in the series of steps of attribute determination processing S 200 . 
     When the attribute determination device  200  detects a new person in the selected image (Yes in S 200 - 3 ), the attribute determination device  200  ends the person detection processing S 200  with the new person detected. 
     On the other hand, when the attribute determination device  200  fails to detect a new person in the selected image (No in S 200 - 3 ), the attribute determination device  200  checks whether or not there is any unselected image (S 200 - 4 ). When there is any unselected image (Yes in S 200 - 4 ), the attribute determination device  200  selects a new image from among the unselected images (S 200 - 1 ). 
     On the other hand, when there is no unselected image (No in S 200 - 4 ), the attribute determination device  200  ends the person detection processing S 200  with no new person detected. 
     Note that, in the detection of a new person (S 200 - 2 ) in the person detection processing S 200 , for example, the attribute determination device  200  extracts a person from the image and analyzes the physical features (e.g., facial features) of the extracted person, and if the person having the analyzed physical features has not been detected as a new person in the past, the attribute determination device  200  determines that a new person has been detected. 
     &lt;Image Analysis Processing&gt; 
       FIG. 9  illustrates an example of a processing flowchart of the image analysis processing S 300 . In the image analysis processing S 300 , the attribute determination device  200  calculates, from the selected image in the person detection processing S 200 , a provisional probability that the detected person has the first attribute (S 300 - 1 ). The provisional probability is a probability, required to calculate (used when calculating) a final probability calculated in the final attribute determination processing S 400  to be executed later, which is calculated based on the image analysis of a person. In the case where the first attribute is whether or not the person is male, for example, the processing S 300 - 1  is processing of analyzing an image of the physical features of the detected person in the selected image and calculating a probability that the detected person is male from the facial features, the height, and the like. 
     The attribute determination device  200  searches the images of the image capturing devices  100  other than the image capturing device  100  (first image capturing device) that has captured the selected image for images in which the detected person appears (S 300 - 2 ). In the processing S 300 - 2 , the attribute determination device  200  recognizes in which of the images of the image capturing devices  100  the detected person appears. 
     When the attribute determination device  200  finds an image or images in which the detected person appears as a result of the search (Yes in S 300 - 3 ), the attribute determination device  200  calculates a provisional probability that the detected person has the first attribute for each image capturing devices  100  (first image capturing device) that has captured the found image (S 300 - 4 ), and then ends the image analysis processing S 300 . The method of calculating the provisional probability is the same as that of the processing S 3001 . 
     On the other hand, when the attribute determination device  200  fails to find any image in which the detected person appears as a result of the search (No in S 300 - 3 ), the attribute determination device  200  ends the image analysis processing S 300 . 
     &lt;Final Attribute Determination Processing&gt; 
       FIG. 10  illustrates an example of a processing flowchart of the final attribute determination processing S 400 . In the final attribute determination processing S 400 , the attribute determination device  200  multiplies each of the provisional probabilities for the image capturing devices  100  calculated in the image analysis processing S 300  by the first attribute score in the attribute score table, and calculates a final probability for each image capturing device  100  (S 400 - 1 ). The final probability for each image capturing device  100  is calculated by, for example, the following Equation (1). 
         PFx=PTx×Sx   Equation (1),
 
     where PFx indicates the final probability for an image capturing device x, PTx indicates the provisional probability for the image capturing device x, and Sx indicates the first attribute score in the attribute score table for the image capturing device x. 
     Then, the attribute determination device  200  calculates an average value of the final probabilities calculated for the respective image capturing devices  100  (S 400 - 2 ). 
     The average value of the final probabilities is calculated using, for example, the following Equation (2). 
         PF ave=( PF 1+ PF 2+ . . . + PFn )/ n   Equation (2),
 
     where PFave indicates the average value of the final probabilities, and n indicates the number of image capturing devices  100  for which the final probabilities have been calculated. 
     Note that the provisional probability indicates a probability that a person has the first attribute based on, for example, image determination. Further, the first attribute score indicates, for example, a probability that the person in the image captured by the corresponding image capturing device  100  has the first attribute, based on statistics. In this case, the final probability is a numerical value obtained by multiplying the probabilities, and the larger the numerical value, the higher the probability of having the first attribute. 
     The attribute determination device  200  compares the average value of the final probabilities with a threshold value (S 400 - 3 ). When the average value of the final probabilities is equal to or greater than the threshold value (Yes in S 400 - 3 ), the attribute determination device  200  determines that the detected person has the first attribute (S 400 - 4 ). On the other hand, when the average value of the final probabilities is not equal to or greater than the threshold value (No in S 400 - 3 ), the attribute determination device  200  determines that the detected person does not have the first attribute (S 400 - 5 ). 
     Then, the attribute determination device  200  adds the attribute determination result of the detected person to the attribute determination result table (S 400 - 6 ), and ends the final attribute determination processing S 400 . 
     &lt;Attribute Score Update Processing&gt; 
       FIG. 11  illustrates an example of a processing flowchart of the attribute score update processing S 500 . The attribute determination device  200  checks whether or not the condition for updating the attribute score is satisfied in the attribute score update processing S 500  (S 500 - 1 ). Then, when the condition for updating the attribute score is satisfied (Yes in S 500 - 1 ), the attribute determination device  200  updates the attribute score table (S 500 - 2 ), and then ends the attribute score update processing S 500 . 
     On the other hand, when the condition for updating the attribute score is satisfied (No in S 500 - 1 ), the attribute determination device  200  ends the attribute score update processing S 500  without updating the attribute score table  225 . 
     The condition for updating is, for example, that the difference between the average value of the final probabilities and the threshold value is equal to or greater than a predetermined value in the final attribute determination processing S 400 . The larger the difference between the average value of the final probabilities and the threshold value, the higher the accuracy of the attribute determination result, and the smaller the difference, the lower the accuracy of the attribute determination result. Accordingly, in order to determine the attribute, the greater the difference between the average value of the final probabilities and the threshold value, the higher the accuracy. Therefore, setting the condition for updating makes it possible to prevent a low accuracy result of determination of the attribute from being reflected in the attribute score. 
     To update the attribute score, for example, the attribute score table  225  is updated. The attribute determination device  200  increases, for example, the total number (first cumulative number) in the attribute score table  225  by one. Then, when the attribute determination device  200  determines that a certain person has the first attribute, the attribute determination device  200  increases the number of people who have the first attribute (second cumulative number) by one. As a result, the attribute score table  225  becomes the latest statistical information, and the accuracy of the attribute score is improved. 
     &lt;Attribute Determination Example&gt; 
     The attribute determination processing S 100  and the transition of each table will be described below with reference to specific numerical values. 
     The attribute determination device  200  executes the attribute determination processing S 100  at a certain timing. Then, the attribute determination device  200  detects Person A as a new person from the image captured by the image capturing device  100 - 1  in the person detection processing S 200  (Yes in S 200 - 3  in  FIG. 8 ). 
     The attribute determination device  200  calculates a provisional probability that Person A appears in the image of the image capturing device  100 - 1  has the first attribute (S 300 - 1  in  FIG. 9 ). The attribute determination device  200  calculates, for example, the provisional probability as 0.55 as a result of the image determination. 
     The attribute determination device  200  searches for other images in which Person A appears (S 300 - 2  in  FIG. 9 ). Then, the attribute determination device  200  finds that Person A appears in the image captured by the image capturing device  100 - 3  (S 300 - 3  in  FIG. 9 ). 
     The attribute determination device  200  calculates a provisional probability that Person A has the first attribute based on the image of the image capturing device  100 - 3  (S 300 - 4  in  FIG. 9 ). The attribute determination device  200  calculates, for example, the provisional probability as 0.40 as a result of the image determination. 
     The attribute determination device  200  calculates a final probability of each of the image capturing device  100 - 1  and the image capturing device  100 - 3  that Person A has the first attribute by multiplying the corresponding provisional probability by the corresponding attribute score (S 400 - 1  in  FIG. 10 ). 
     Using the attribute score examples in the attribute score table  225  in  FIG. 6 , the final probability for the image capturing device  100 - 1  is 0.385 (=0.55×0.7), and the final probability for the image capturing device  100 - 3  is 0.3 (=0.4×0.75). 
     The attribute determination device  200  calculates an average value of the final probabilities, 0.3425 (=(0.385+0.3)/2) (S 400 - 2  in  FIG. 10 ). The attribute determination device  200  compares the calculated average value of the final probabilities with the threshold value (S 400 - 3  in  FIG. 10 ). 
     For example, for an attribute having two elements to be determined such as whether or not the person is male, the threshold value is 0.25 which is a numerical value obtained by multiplying the probability of being male, half, i.e., 0.5, based on the image determination by 0.5 indicating the attribute score of a location where the male and female ratios are the same. 
     Since the average value of the final probabilities, 0.3425, is equal to or greater than the threshold value, 0.25 (Yes in S 400 - 3  in  FIG. 10 ), the attribute determination device  200  determines that Person A has the first attribute (S 400 - 4  in  FIG. 10 ). The attribute determination device  200  adds the result of determining the attribute of Person A to the attribute determination result table (S 400 - 6  in  FIG. 10 ). 
       FIG. 12  illustrates an example of the attribute determination result table  226  after the update. The updated part is shaded. The attribute determination device  200  adds that Person A is the first attribute to the end of the attribute determination result table  226 . 
     The attribute determination device  200  checks whether or not the attribute score is to be updated (S 500 - 1  in  FIG. 11 ). When the attribute determination device  200  determines that the attribute score is to be updated (Yes in S 500 - 1  in  FIG. 11 ), the attribute determination device  200  updates the attribute score table (S 500 - 2  in  FIG. 11 ). 
       FIG. 13  illustrates an example of the attribute score table  225  after the update. The updated part is shaded. The attribute determination device  200  increases the total number for the image capturing device  100 - 1  in the attribute score table  225  by one, and increases the number of people who have the first attribute by one. Then, the attribute determination device  200  calculates the attribute score of the first attribute for the image capturing device  100 - 1  as 0.71 (15/21), and updates the attribute score table  225 . Similarly, the attribute determination device  200  updates the total number, the first attribute, and the attribute score of the first attribute for the image capturing device  100 - 3  in the attribute score table  225 . 
     Then, the attribute determination device  200  searches for a person other than Person A in the image (S 200 - 1  and - 2  in  FIG. 8 ), and executes the same processing until a new person fails to be detected. 
     In the first embodiment, the attribute determination device  200  improves the accuracy of whether or not a person has a certain attribute based on the probability (provisional probability) that the person has the certain attribute, which is calculated from image determination based on the image(s) of the person, and the attribute score for the image capturing device  100  that has captured the person. 
     For example, in the above-mentioned specific examples, if any attribute score is not taken into consideration, it will be determined whether or not Person A has the first attribute by using only a provisional probability of 0.55 from the image of the image capturing device  100 - 1  and a provisional probability of 0.4 from the image of the image capturing device  100 - 3 . In this case, for example, the average value of the provisional probabilities is 0.475, which is a probability of 0.5 or less for the first attribute, and thus this may result in a determination that the person does not have the first attribute, which is different from the attribute determination result in the first embodiment. 
     OTHER EMBODIMENTS 
     In the first embodiment, only one attribute (first attribute) is used as the determination target. However, a plurality of types of attributes may be used as determination targets.  FIG. 14  illustrates an example of the attribute determination result table  226  in the case of determining a plurality of types of attributes. The attribute determination device  200  also determines other types of attributes in the same manner as the determination of the first attribute in the first embodiment described above, and reflects the determination results in the attribute determination result table  226 . The types of attributes may include, for example, physical features such as age and height, and preferences such as the color and shape of hats and clothes. 
     Further,  FIG. 15  illustrates an example of the attribute score table  225  in the case of determining a plurality of types of attributes. The attribute determination device  200  updates the attribute score table  225  not only for the total number and the first attribute but also for the number of people who has the second attribute and those for the other attributes. Note that, in the case where a plurality of types of attributes are used as determination targets, the condition for updating in the attribute score update processing S 500  is determined comprehensively based on the determination results of the types of attributes. 
     Further, in the first embodiment, the attribute score is the ratio (statistical score) of people who have a certain attribute to the total number of people who appear in the image captured by the image capturing device  100 . However, the attribute score may be a numerical value according to the characteristics of the image capturing device  100 . For example, when the image capturing device  100  captures an image of the venue of a seminar for men, the first attribute score for the image capturing device  100  may be a numerical value as close as possible to 1.0. In other words, the attribute score may a numerical value according to the characteristics of a location where the image capturing device  100  captures an image, instead of a statistical score. Further, the attribute score may be a numerical value in consideration of both the statistical score and the characteristics of the image capturing device  100 . 
     Further, the method of calculating the final probability is not limited to Equation (1). The final probability can be a numerical value in which both the attribute score for the image capturing device  100  and the probability (provisional probability) based on the image determination are taken into consideration. For example, the final probability may be calculated by addition instead of multiplication. 
     Further, in the first embodiment, the attribute score is a probability, but may be a statistical value. For example, the total number and the number for the first attribute in the attribute score table  225  may be referred to as attribute scores. 
     One disclosure improves the accuracy of determination for the object whose attribute is to be determined appearing in the image. 
     All examples and conditional language provided herein are intended for the pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventor to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although one or more embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention. 
     REFERENCE SIGNS LIST 
     
         
         
           
               10  Attribute determination system 
               100  Image capturing device 
               110  CPU 
               120  Storage 
               121  Image capturing program 
               122  Image transmission program 
               130  Memory 
               160  Camera 
               200  Attribute determination device 
               210  CPU 
               220  Storage 
               221  Image acquisition program 
               222  Attribute determination program 
               2221  Person detection module 
               2222  Image analysis module 
               2223  Final attribute determination module 
               223  Attribute score update program 
               224  Image data table 
               225  Attribute score table 
               226  Attribute determination result table 
               230  Memory 
               250  Display 
               1001  Image capturing unit 
               1002  Image transmission unit 
               2001  Image acquisition unit 
               2002  Image analysis unit 
               2003  Attribute determination unit 
               2004  Attribute score update unit