Risk value calculation device, risk value calculation method, and risk value calculation program

Abstract

This risk value calculation device (11) comprises: an image data acquisition unit (110), a temperature measurement unit (111), a symptom detection unit (112), a risk value calculation unit (113), and an output unit (114). The image data acquisition unit (110) acquires visible light image data generated by a visible light camera, and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data. The temperature measurement unit (111) specifies a person included in the visible light image data and the thermal image data, and measures the body surface temperature of the specified person. The symptom detection unit (112) detects the symptom of the person's infectious disease, on the basis of the visible light image data. The risk value calculation unit (113) calculates an infection risk value of the person, on the basis of the body surface temperature and the symptom. The output unit (114) outputs the infection risk value.

Description

[Technical field]

[0001] The present invention relates to a risk value calculation device, a system, a method, and a program. [Background technology]

[0002] 2. Description of the Related Art Techniques are known for detecting or measuring the risk of infection with an infectious disease among people present in a given space.

[0003] For example, the infection risk identification system described in Patent Document 1 identifies the infection level of people in a space, detects the positions of the people in the space, and generates information indicating the risk of infection with infectious substances for each position in the space based on the infection level and the positions of the people.

[0004] Furthermore, Patent Document 2 describes a technology that identifies people from video camera footage at areas with high pedestrian traffic, measures their body temperatures with a thermal imaging camera, and selects people with body temperatures above a predetermined threshold to presume they have a fever. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2020-067939 A [Patent Document 2] JP 2012-235415 A Summary of the Invention [Problem to be solved by the invention]

[0006] Among the above technologies, for example, when measuring a person's body surface temperature using a thermal camera, there are the following problems. First, a person infected with an infectious disease does not necessarily have a fever. Second, if the part of the person's body that is to be measured by the thermal camera is not exposed or is significantly affected by the outside temperature, the body surface temperature may not be measured properly. There are also attempts to measure the breathing and heart rate of a person passing through a specified gate and analyze the measured breathing and heart rate together with the person's fever status. However, when such a technology is adopted, it is not possible to measure the breathing and heart rate of multiple people in a large space.

[0007] The present disclosure has been made in consideration of such problems, and aims to provide a risk value calculation device etc. that can appropriately determine the risk of infection. [Means for solving the problem]

[0008] A risk value calculation device according to an embodiment of the present disclosure includes image data acquisition means, temperature measurement means, symptom detection means, risk value calculation means, and output means. The image data acquisition means acquires visible light image data generated by a visible light camera and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data. The temperature measurement means identifies a person included in the visible light image data and the thermal image data, and measures the body surface temperature of the identified person. The symptom detection means detects symptoms of an infectious disease of the person based on the visible light image data. The risk value calculation means calculates an infection risk value of the person based on the body surface temperature and the symptoms. The output means outputs the infection risk value.

[0009] In a determination method according to an embodiment of the present disclosure, a computer executes the following method. The computer acquires visible light image data generated by a visible light camera and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data. The computer identifies a person included in the visible light image data and the thermal image data, and measures the body surface temperature of the identified person. The computer detects symptoms of an infectious disease of the person based on the visible light image data. The computer calculates an infection risk value of the person based on the body surface temperature and the symptoms. The computer outputs the infection risk value.

[0010] A program according to an embodiment of the present disclosure causes a computer to execute the following steps: The computer acquires visible light image data generated by a visible light camera and thermal imaging data generated by an infrared camera having a shooting range corresponding to at least a portion of the visible light image data. The computer identifies a person included in the visible light image data and the thermal imaging data, and measures the body surface temperature of the identified person. The computer detects symptoms of an infectious disease of the person based on the visible light image data. The computer calculates an infection risk value of the person based on the body surface temperature and the symptoms. The computer outputs the infection risk value. Effect of the Invention

[0011] According to the present disclosure, it is possible to provide a risk value calculation device etc. that can appropriately determine the risk of infection. [Brief description of the drawings]

[0012] [Figure 1] 1 is a block diagram showing a configuration of a risk calculation device according to a first embodiment. [Diagram 2] 3 is a flowchart showing a risk calculation method according to the first embodiment. [Diagram 3] FIG. 11 is a block diagram showing a configuration of a risk calculation device according to a second embodiment. [Figure 4] 10 is a flowchart showing a risk calculation method according to a second embodiment. [Diagram 5] FIG. 11 is a block diagram showing a configuration of a risk calculation device according to a third embodiment. [Figure 6] 13 is a flowchart showing a risk calculation method according to a third embodiment. [Figure 7] FIG. 13 is a block diagram showing a configuration of a risk calculation system according to a fourth embodiment. [Figure 8] FIG. 13 is a block diagram showing a configuration of an authentication device according to a fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In each drawing, the same or corresponding elements are denoted by the same reference numerals, and duplicated descriptions will be omitted as necessary for clarity of explanation.

[0014] <Embodiment 1> The first embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the configuration of a risk value calculation device 11 according to the first embodiment. The risk value calculation device 11 is used, for example, to ascertain the possibility that a person present in a specified facility is infected with a specified infection or the possibility of infection. The risk value calculation device 11 mainly comprises an image data acquisition unit 110, a temperature measurement unit 111, a symptom detection unit 112, a risk value calculation unit 113, and an output unit 114. These components of the risk value calculation device 11 are connected to each other so as to be able to communicate with each other as appropriate.

[0015] The image data acquisition unit 110 acquires visible light image data generated by a visible light camera and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data. The image data acquisition unit 110 can supply the acquired visible light image data and thermal image data to the temperature measurement unit 111 and the symptom detection unit 112, respectively.

[0016] The visible light camera and infrared camera are installed in a predetermined location inside or around a predetermined facility or outdoors. The visible light camera photographs a scene including a person, generates visible light image data related to the photographed image of the scene, and supplies the generated visible light image data to the risk value calculation device 11. The infrared camera photographs a scene including a person, generates thermal image data related to the photographed image of the scene, and supplies the generated thermal image data to the risk value calculation device 11.

[0017] At least a part of the shooting range of the visible light camera and the infrared camera overlap each other. In other words, the infrared camera has a shooting range corresponding to at least a part of the visible light image captured by the visible light camera. In addition, it is preferable that the visible light camera and the infrared camera are fixed so that their positional relationship does not change. With such a configuration, the shooting device can associate an object included in the visible light image data generated by the visible light camera with an object included in the thermal image data generated by the infrared camera. Note that the visible light camera and the infrared camera may each be one or more. For example, the visible light camera or the infrared camera may be movable so that their positional relationship changes. In this case, it is preferable that the visible light camera or the infrared camera automatically returns to a predetermined position even if the angle of view is changed by temporarily panning, tilting, or zooming due to, for example, a user's operation.

[0018] The temperature measurement unit 111 identifies a person included in the visible light image data and the thermal imaging data, and measures the body surface temperature of the identified person. After measuring the body surface temperature of the person, the temperature measurement unit 111 supplies data related to the measured body surface temperature to the risk value calculation unit 113.

[0019] More specifically, for example, the temperature measurement unit 111 first extracts an image of a person from the visible light image data. At this time, the temperature measurement unit 111 may extract only a specific part of the person's body, such as a facial image. When the visible light camera captures images of multiple people at the same time, the temperature measurement unit 111 extracts each of the multiple people from the visible light image data and identifies each of them.

[0020] Next, the temperature measurement unit 111 extracts thermal imaging data corresponding to the image of the identified person. Furthermore, the temperature measurement unit 111 measures the body surface temperature of the identified person from the extracted thermal imaging data. The temperature measurement unit 111 may measure the body surface temperature from a part showing the highest temperature among the extracted thermal imaging data. The temperature measurement unit 111 may also calculate a statistical value of the temperature of the extracted thermal imaging data, and set the calculated statistical value as the body surface temperature of the person.

[0021] When the symptom detection unit 112 receives the visible light image data, it detects symptoms of an infectious disease of a person from the image data of the person included in the received visible light image data. When the symptom detection unit 112 detects symptoms from the image data of the person, it supplies data indicating that the symptoms have been detected to the risk value calculation unit 113.

[0022] More specifically, the symptom detection unit 112 identifies image data of a person included in the visible light image data. Next, the symptom detection unit 112 analyzes the image data of the identified person to estimate the posture of the person. Furthermore, the symptom detection unit 112 determines whether the estimated posture matches a symptom exhibited when the person is suffering from a preset disease. Then, when the symptom detection unit 112 determines that the posture of the person matches the symptom, it detects the symptom from the identified person.

[0023] The preset disease is, for example, pneumonia, more specifically, infectious pneumonia. Infectious pneumonia includes viral pneumonia, bacterial pneumonia, and mycoplasma pneumonia. When a person infected with such pneumonia develops pneumonia, the infected person may, for example, hold both elbows due to chills, lean forward, or place hands on the throat or chest due to pain in the throat or chest. The preset disease may be other diseases that have specific symptoms.

[0024] The risk value calculation unit 113 calculates an infection risk value of the identified person from the body surface temperature and symptoms of the person. After calculating the infection risk value of the person, the risk value calculation unit 113 supplies the calculated infection risk value to the output unit 114. Note that the symptom detection unit 112 may detect symptoms using a thermal image acquired from the thermal camera 92 in addition to the visible light image data acquired from the visible light camera.

[0025] More specifically, the risk value calculation unit 113 receives data on the person's body surface temperature from the temperature measurement unit 111. The risk value calculation unit 113 also receives data on the person's symptoms from the symptom detection unit 112. The risk value calculation unit 113 then uses these received data to calculate the person's infection risk value. The infection risk value is an index for estimating infection with a preset disease, and is indicated by a preset numerical value. For example, the infection risk value is defined by a numerical value from 0 to 1. In this case, for example, 0 means the lowest infection risk, and 1 means the highest infection risk. Note that the higher the "infection risk", the higher the possibility of infection. More specifically, for example, the person's body surface temperature is classified into a plurality of levels, and each classification is set to a preset value within the range from 0 to 1. Also, the person's symptoms are set to a preset value within the range from 0 to 1 for each corresponding symptom. The infection risk value is calculated by taking the average value of each value set in this way. When the person's body surface temperature exceeds a predetermined threshold, the infection risk value is calculated to be higher than when the threshold is not exceeded. In addition, if the person shows symptoms of an infectious disease, the infection risk value is calculated to be higher than if the person does not show the symptoms. Note that the above-mentioned method of calculating the infection risk value is an example, and the method of calculating the infection risk value is not limited to the above.

[0026] When the output unit 114 receives the infection risk value calculated by the risk value calculation unit 113, it outputs the received infection risk value to a predetermined display device. The predetermined display device may be included in the risk value calculation device 11. The predetermined display device may be included in a predetermined terminal device communicatively connected to the risk value calculation device 11.

[0027] Next, the process executed by the risk value calculation device 11 according to the embodiment 1 will be described with reference to Fig. 2. Fig. 2 is a flowchart showing a risk calculation method according to the embodiment 1. The flowchart shown in Fig. 2 starts, for example, by starting up the risk value calculation device 11.

[0028] First, the image data acquisition unit 110 acquires visible light image data from the visible light camera and thermal image data from the infrared camera (step S11). The image data acquisition unit 110 may acquire the visible light image data and the thermal image data in parallel, or may acquire them sequentially according to a preset protocol.

[0029] Next, the temperature measurement unit 111 identifies people included in the visible light image data and the thermal imaging data (step S12), and measures the body surface temperature of the identified people (step S13). When the temperature measurement unit 111 measures the body surface temperatures of multiple people, it generates body surface temperature data in a manner that allows the risk value calculation unit 113 to identify each of the identified people.

[0030] Next, the symptom detection unit 112 detects symptoms of an infectious disease in the person based on the visible light image data (step S14). When the symptom detection unit 112 detects symptoms in multiple people, the symptom detection unit 112 generates data on the symptoms in a manner that allows the risk value calculation unit 113 to identify each of the specified people.

[0031] Next, the risk value calculation unit 113 calculates an infection risk value of the identified person from the data on the body surface temperature received from the temperature measurement unit 111 and the data on the symptoms received from the symptom detection unit 112 (step S15).

[0032] Next, the output unit 114 receives the infection risk value calculated by the risk value calculation unit 113, and outputs the received infection risk value (step S16).

[0033] The above describes the process executed by the risk value calculation device 11. The risk value calculation method according to this embodiment is not limited to the above-mentioned flowchart. For example, steps S13 and S14 may be executed in parallel, or step S12 may be executed after step S13.

[0034] The first embodiment has been described above. As described above, the risk value calculation device 11 according to the first embodiment uses visible light image data acquired from a visible light camera and thermal imaging data acquired from a thermal camera. The risk value calculation device 11 calculates an infection risk value by combining a person's body surface temperature measured from the thermal imaging data and the person's symptoms detected from the visible light image data. Thus, according to this embodiment, it is possible to provide a risk value calculation device, a risk value calculation method, and a program that can appropriately determine the infection risk.

[0035] The risk value calculation device 11 has a processor and a storage device as components not shown in the figure. The storage device of the risk value calculation device 11 includes a storage device including a non-volatile memory such as a flash memory or an SSD (Solid State Drive).

[0036] A computer program (hereinafter, simply referred to as a program) for executing the risk value calculation method according to this embodiment is stored in a storage device of the risk value calculation device 11. In addition, the processor loads the computer program from the storage device into the memory and executes the program.

[0037] Each of the components of the risk value calculation device 11 may be realized by dedicated hardware. Also, some or all of the components may be realized by general-purpose or dedicated circuits, processors, etc., or a combination of these. These may be configured by a single chip, or may be configured by multiple chips connected via a bus. Some or all of the components of each device may be realized by a combination of the above-mentioned circuits, etc., and programs. Also, a CPU (Central Processing Unit), GPU (Graphics Processing Unit), FPGA (field-programmable gate array), etc. can be used as the processor.

[0038] Furthermore, when some or all of the components of the risk value calculation device 11 are realized by a plurality of arithmetic devices, circuits, etc., the plurality of arithmetic devices, circuits, etc. may be arranged in a centralized manner or in a distributed manner. For example, the arithmetic devices, circuits, etc. may be realized in a form in which each is connected via a communication network, such as a client-server system or a cloud computing system. Furthermore, the functions of the risk value calculation device 11 may be provided in a SaaS (Software as a Service) format.

[0039] <Embodiment 2> Next, a second embodiment will be described. Fig. 3 is a block diagram showing the configuration of a risk value calculation device according to the second embodiment. The risk value calculation device 12 shown in Fig. 3 is connected to a user terminal 21, a visible light camera 91, and a thermal camera 92 via a network 500 so that they can communicate with each other. The network 500 is a communication network such as a wide area network like the Internet or a local area network like a line within a specified facility.

[0040] The visible light camera 91 and the thermal camera 92 are installed in a predetermined facility 90, and are communicatively connected to the risk value calculation device 12 via the network 500. People are present in the facility 90. The visible light camera 91 and the thermal camera 92 each capture images of the interior scenery including the people in the facility 90, and transmit the captured image data (visible light image data and thermal image data) to the risk value calculation device 12, respectively.

[0041] The user terminal 21 is an information processing terminal such as a personal computer, a smartphone, or a tablet terminal. The user terminal 21 is communicably connected to the risk value calculation device 12 via the network 500, and receives an infection risk value from the risk value calculation device 12. The user terminal 21 displays the infection risk value received from the risk value calculation device 12, and notifies the user operating the user terminal 21 of the infection risk value. The user can grasp the infection risk value of a person present in the facility 90 via the user terminal 21. This allows the user to take action to reduce the infection risk for a person present in the facility 90, for example.

[0042] The risk value calculation device 12 according to the present embodiment differs from the risk value calculation device 11 according to the first embodiment in that it includes a storage unit 120. The storage unit 120 is a storage device including a non-volatile memory such as an EPROM (Erasable Programmable Read Only Memory) or a flash memory.

[0043] The storage unit 120 stores the symptom DB 121. The symptom DB 121 includes information on the symptoms of a preset disease. More specifically, the symptom DB 121 includes symptoms related to the posture of an affected person among the symptoms of a preset disease. The symptom DB 121 also includes information on a form that can be matched with the posture of a person extracted by the symptom detection unit 112. The form that can be matched with the extracted posture of a person is, for example, image data showing a posture pattern of a person or feature amount data extracted from a feature amount of a posture pattern of a person. The symptom DB 121 may include symptom attribute information associated with the posture pattern to be stored. The symptom attribute information is information indicating the possibility of infection or the severity of a symptom corresponding to each posture pattern of a symptom. The storage unit 120 supplies the symptom DB 121 to the symptom detection unit 112.

[0044] The symptom detection unit 112 according to this embodiment reads the symptom DB 121 from the storage unit 120, and compares the read symptom DB 121 with the extracted posture of the person. As a result of the comparison, the symptom detection unit 112 determines whether the extracted posture of the person matches a posture pattern indicating a symptom included in the symptom DB 121. When the symptom detection unit 112 determines that the extracted posture of the person is included in the symptom DB 121, it detects a symptom from the posture of this person. For example, the symptom detection unit 112 detects a symptom when the posture of the person and the posture pattern included in the symptom DB 121 match at a predetermined rate or more within a predetermined period. Specifically, for example, the symptom detection unit 112 detects a symptom when the posture matches a posture pattern indicating a symptom for 10 seconds out of 60 seconds.

[0045] When the symptom DB 121 includes symptom attribute information, the symptom detection unit 112 may supply the symptom attribute information together with the detected symptoms to the risk value calculation unit 113. This allows the risk value calculation device 12 to improve the accuracy of the calculated infection risk value.

[0046] Next, the process of the risk value calculation device 12 according to the second embodiment will be described with reference to Fig. 4. Fig. 4 is a flowchart of the risk calculation method according to the second embodiment. The flowchart shown in Fig. 4 differs from the flowchart according to the first embodiment shown in Fig. 2 in that the process between step S13 and step S15 is step S21 and step S22 instead of step S14.

[0047] Steps S11 to S13 are the same as those in the flowchart shown in Fig. 2. In step S21, the symptom detection unit 112 extracts the posture of the identified person from the visible light image data received from the image data acquisition unit 110 (step S21).

[0048] Next, the symptom detection unit 112 reads the symptom DB 121 from the storage unit 120, compares the extracted posture of the person with the symptom DB 121, and detects symptoms (step S22). If the comparison result between the extracted posture of the person and the symptom DB 121 indicates a match, the symptom detection unit 112 detects symptoms from the extracted posture of the person. If the comparison result between the extracted posture of the person and the symptom DB 121 indicates a match, the symptom detection unit 112 does not detect symptoms from the extracted posture of the person. The symptom detection unit 112 supplies data on the detected symptoms to the risk value calculation unit 113. Next, the risk value calculation device 12 proceeds to step S15. The process from step S15 onwards is the same as that in the flowchart shown in FIG. 2.

[0049] Although the second embodiment has been described above, the risk value calculation device 12 is not limited to the above-mentioned configuration. One visible light camera 91 and one thermal camera 92 are shown in Fig. 3. However, the risk value calculation device 12 may acquire image data from a plurality of visible light cameras 91 and thermal cameras 92. In this case, the number of visible light cameras 91 and thermal cameras 92 may differ.

[0050] The second embodiment has been described above. As described above, the risk value calculation device 12 according to the second embodiment compares the extracted posture of a person with information in the symptom DB 121. This allows the risk value calculation device 12 to accurately calculate an infection risk value. Note that the symptom DB 121 may be configured to be updatable. This allows the risk value calculation device 12 to compare symptoms according to the type of prevalent disease. Therefore, according to the present embodiment, it is possible to provide a risk value calculation device, a risk value calculation method, and a program that can suitably and flexibly determine an infection risk.

[0051] <Embodiment 3> Next, a third embodiment will be described. Fig. 5 is a block diagram showing the configuration of a risk value calculation device according to the third embodiment. The risk value calculation device 13 shown in Fig. 5 differs from the above-mentioned risk value calculation device 11 and risk value calculation device 12 in that it includes a congestion degree calculation unit 115. The image data acquisition unit 110 according to this embodiment also supplies the acquired visible light image data to the congestion degree calculation unit 115.

[0052] The congestion degree calculation unit 115 receives the visible light image data from the image data acquisition unit 110, and calculates the congestion degree of the space from the received visible light image data. The congestion degree of the space is calculated based on the number of people present in a predetermined space. When the congestion degree is high, the number of people present in the space is greater than when the congestion degree is low. That is, the congestion degree calculation unit 115 can calculate the congestion degree by counting the number of people in the predetermined space. For example, the congestion degree calculation unit 115 may set the entire visible light image data as the predetermined space, or may set a part of the image included in the visible light image data as the predetermined space. The congestion degree calculation unit 115 may also divide the space included in the visible light image data into multiple spaces and calculate the congestion degree for each divided space. The congestion degree calculation unit 115 supplies the calculated congestion degree to the risk value calculation unit 113.

[0053] Alternatively, instead of the above-mentioned method, the congestion degree calculation unit 115 may treat people included in a predetermined space as a crowd and calculate the congestion degree from the state of the crowd. A crowd refers to a state in which multiple people appear to overlap in a predetermined space. In this case, the congestion degree calculation unit 115 detects a crowd and identifies the congestion state of the detected crowd. In this way, the congestion degree calculation unit 115 calculates the congestion degree. More specifically, for example, the congestion degree calculation unit 115 extracts a predetermined local image smaller than the image size of the visible light image data, and determines the congestion state of the crowd in the extracted local image. When determining such a congestion state, the congestion degree calculation unit 115 may use a classifier generated by machine learning, for example. By adopting such a means, the congestion degree calculation unit 115 can calculate the congestion degree without counting the exact number of people in the image.

[0054] In the above example, the congestion degree calculation unit 115 uses visible light image data when calculating the congestion degree. However, the data used in the congestion degree calculation performed by the congestion degree calculation unit 115 is not limited to visible light image data. In other words, the congestion degree calculation unit 115 may calculate the congestion degree using thermal imaging data.

[0055] The risk value calculation unit 113 calculates the infection risk value by taking into account the body surface temperature and symptoms of the identified person, as well as the degree of congestion of a predetermined space in which the person is included in the visible light image data. That is, the risk value calculation unit 113 calculates the infection risk value of a person present in a space with a relatively high degree of congestion to be higher than the infection risk value of a person present in a space with a relatively low degree of congestion. By calculating the infection risk value by taking the degree of congestion into account in this way, the risk value calculation device 13 can improve the accuracy of the infection risk value.

[0056] The risk value calculation unit 113 may calculate the infection risk value by further taking into account the conditions of surrounding people as follows. The risk value calculation unit 113 may calculate the infection risk value of a person by further taking into account the body surface temperature and symptoms of surrounding people who are present around the person. More specifically, for example, when surrounding people are present in the vicinity of the person whose infection risk value is being calculated, if the body surface temperature of the surrounding people exceeds normal body temperature or if the posture of the surrounding people matches the posture pattern of the symptoms, the risk value calculation unit 113 takes this into account and calculates a relatively high infection risk value.

[0057] Furthermore, when the infection risk value of the surrounding person has already been calculated, the risk value calculation unit 113 calculates the infection risk value taking into account the infection risk value of the surrounding person. For example, when the infection risk value of the surrounding person is the first risk value (e.g., 0.7), the risk value calculation unit 113 calculates the infection risk value to be relatively higher than when the infection risk value of the surrounding person is the second risk value (e.g., 0.3).

[0058] The risk value calculation unit 113 may calculate the infection risk value of the person by further taking into account the distance between the person and the surrounding people around the person. More specifically, for example, when the distance between the person and the surrounding people is a first distance (for example, 1 meter), the risk value calculation unit 113 calculates the infection risk value of the surrounding people to be relatively higher than the infection risk value calculated when the distance is a second distance (for example, 3 meters) that is longer than the first distance. In light of the above, for example, the infection risk value may be calculated as follows. First, each element for calculating the infection risk value is set to a value between 0 and 1 as follows. For example, the body surface temperature of the person is classified into a plurality of levels, and each classification is set to a value set in advance within the range from 0 to 1. Also, the symptoms of the person are set to a value set in advance within the range from 0 to 1 for each corresponding symptom, for example. Also, the congestion degree is normalized so that the more crowded it is, the closer it is to 1, and the less crowded it is, the closer it is to 0. The infection risk value is calculated by taking the average value of each value set in this way. Similarly, the body surface temperature of the surrounding people and the symptoms of the surrounding people are set to a value between 0 and 1. Then, the risk value calculation unit 113 calculates the average value of these elements as the infection risk value. When calculating the average value, the risk value calculation unit 113 may weight the values ​​so that the influence of a specific element is reflected relatively largely. The above-mentioned infection risk value indicates the risk of infection spreading to surrounding people around a specific person. However, the risk value calculation unit 113 may calculate the risk of infection spreading from surrounding people to the specific person, or may calculate the risk of infection spreading in a specific crowd. The above-mentioned method of calculating the infection risk value is one example, and the method of calculating the infection risk value is not limited to the above-mentioned one.

[0059] Next, the process of the risk value calculation device 13 according to the third embodiment will be described with reference to Fig. 6. Fig. 6 is a flowchart of the risk calculation method according to the third embodiment. The flowchart shown in Fig. 6 differs from the flowchart according to the second embodiment shown in Fig. 4 in that it has steps S31 and S32 instead of step S15.

[0060] After step S22, in step S31, the congestion degree calculation unit 115 calculates the congestion degree from the visible light image data (step S31).

[0061] Next, risk value calculation unit 113 calculates an infection risk value of the identified person from the data on body surface temperature received from temperature measurement unit 111, the data on symptoms received from symptom detection unit 112, and the congestion degree received from congestion degree calculation unit 115 (step S32). When risk value calculation unit 113 calculates the infection risk value, output unit 114 outputs the infection risk value received from risk value calculation unit 113 (step S16).

[0062] The flowchart according to the third embodiment has been described above. Note that step S31 may be executed between step S11 and step S32 at a timing different from the order shown in FIG. 6. Also, the process of measuring body surface temperature (step S13), the process of detecting a person's symptoms (steps S21 and S22), and the process of calculating the congestion degree (step S31) may be executed in any order. Also, these processes may be executed in parallel.

[0063] The third embodiment has been described above. The risk value calculation device 13 according to the third embodiment calculates the degree of congestion, and calculates a risk value taking the calculated degree of congestion into account. This allows the risk value calculation device 12 to accurately calculate an infection risk value. Furthermore, the risk value calculation unit 113 can calculate an infection risk value with even greater accuracy by further taking into account the situations of people around the person whose infection risk value is being calculated. Thus, according to this embodiment, it is possible to provide a risk value calculation device, a risk value calculation method, and a program that can appropriately and accurately determine an infection risk.

[0064] <Embodiment 4> Next, a fourth embodiment will be described. The fourth embodiment is a system including at least a risk value calculation device and an authentication device. FIG. 7 is a block diagram showing the configuration of a risk calculation system according to the fourth embodiment. FIG. 7 shows a risk value calculation system 700. The risk value calculation system 700 includes a risk value calculation device 14, an authentication device 200, a user terminal 21, a visible light camera 91, and a thermal camera 92.

[0065] The risk value calculation device 14 according to this embodiment is communicatively connected to the authentication device 200 via the network 500. When identifying a person, the risk value calculation device 14 cooperates with the authentication device 200 to also authenticate the person. By performing authentication, the risk value calculation device 11 can use attribute information linked to the identified person. This allows the risk value calculation device 11 to store the history of infection risk values ​​for the identified person and directly notify the individual of the calculated infection risk value.

[0066] The storage unit 120 of the risk value calculation device 14 stores attribute information 122. The attribute information includes attribute information associated with the person to be authenticated. The attribute information 122 includes personal information such as the person's name and contact information. The attribute information 122 may also include information regarding the medical history and chronic diseases of the person to be authenticated. The risk value calculation device 14 may use the attribute information when calculating the infection risk value. For example, the risk value calculation unit 113 may calculate a relatively high infection risk value for a person with a chronic immune disease.

[0067] Authentication device 200 is communicatively connected to risk value calculation device 14 via network 500. Authentication device 200 authenticates a person based on the person's face image data in cooperation with risk value calculation device 14. More specifically, authentication device 200 receives visible light image data from risk value calculation device 14, and performs authentication on the face image included in the received visible light image data. Authentication device 200 also supplies the result of the authentication to risk value calculation device 14.

[0068] Next, the configuration of the authentication device 200 will be described in detail with reference to Fig. 8. Fig. 8 is a block diagram showing the configuration of the authentication device 200. The authentication device 200 has a facial feature DB 210, a face detection unit 220, a feature point extraction unit 230, a registration unit 240, and an authentication unit 250.

[0069] The facial feature DB 210 is a facial feature database that stores a person's user ID in association with the facial feature information of the person. The face detection unit 220 detects a facial area included in a photographed image and outputs the detected area to the feature point extraction unit 230. The feature point extraction unit 230 extracts feature points from the facial area detected by the face detection unit 220 and outputs the facial feature information to the registration unit 240. The facial feature information is a collection of the extracted feature points.

[0070] The registration unit 240 issues a new user ID when registering the facial feature information. The registration unit 240 associates the issued user ID with the facial feature information extracted from the registration image and registers it in the facial feature DB 210. The authentication unit 250 compares the facial feature information extracted from the face image with the facial feature information in the facial feature DB 210. If the facial feature information matches, the authentication unit 250 determines that the facial authentication has been successful, and if the facial feature information does not match, the authentication unit 250 determines that the facial authentication has failed. The authentication unit 250 returns the success or failure of the facial authentication to the risk value calculation device 14. The presence or absence of a match in the facial feature information corresponds to the success or failure of the authentication. Furthermore, if the facial authentication is successful, the authentication unit 250 identifies the user ID associated with the successful facial feature information, and returns the authentication result including the identified user ID and a notice of the successful authentication to the risk value calculation device 14.

[0071] Although the fourth embodiment has been described above, the risk value calculation system 700 according to the fourth embodiment is not limited to the above-mentioned configuration. For example, the risk value calculation system 700 may be an integrated system of the risk value calculation device 14 and the authentication device 200. In the risk value calculation system 700, the user terminal 21 may be a terminal of a person to be authenticated. In that case, the user terminal 21 is possessed by each of the multiple persons to be authenticated. Therefore, the risk value calculation system 700 may have multiple user terminals 21 corresponding to the multiple persons to be authenticated.

[0072] The fourth embodiment has been described above. The risk value calculation system 700 according to the fourth embodiment can authenticate a person and calculate an infection risk value for the authenticated person. Thus, the risk value calculation system 700 can calculate an infection risk value that takes personal information into account. Alternatively, the risk value calculation system 700 can notify an individual of the calculated infection risk value. Thus, according to this embodiment, it is possible to provide a risk value calculation device, a risk value calculation method, and a program that can appropriately determine an infection risk while taking personal information into consideration.

[0073] The above-mentioned program can be stored and supplied to a computer using various types of non-transitory computer-readable media. The non-transitory computer-readable media includes various types of tangible recording media. Examples of the non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROM (Read Only Memory), CD-R, CD-R / W, and semiconductor memory (e.g., mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (Random Access Memory)). The program may also be supplied to a computer by various types of temporary computer-readable media. Examples of the temporary computer-readable media include electric signals, optical signals, and electromagnetic waves. The temporary computer-readable media can supply the program to a computer via a wired communication path such as an electric wire and an optical fiber, or via a wireless communication path.

[0074] The present invention is not limited to the above-described embodiment, and can be modified as appropriate without departing from the spirit and scope of the present invention.

[0075] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes. (Appendix 1) an image data acquisition means for acquiring visible light image data generated by a visible light camera and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data; a temperature measuring means for identifying a person included in the visible light image data and the thermal image data and measuring a body surface temperature of the identified person; a symptom detection means for detecting a symptom of an infectious disease of the person based on the visible light image data; a risk value calculation means for calculating an infection risk value of the person based on the body surface temperature and the symptoms; and an output means for outputting the infection risk value. Risk value calculation device. (Appendix 2) the symptom detection means detects the symptom from the posture of the person. 2. A risk value calculation device according to claim 1. (Appendix 3) A memory unit that stores a posture pattern associated with a symptom of the infectious disease, the symptom detection means detects the symptom by comparing the posture of the person with the posture pattern. 3. A risk value calculation device according to claim 2. (Appendix 4) the symptom detection means detects the symptom when the posture of the person and the posture pattern match at least a predetermined rate within a predetermined period of time. 4. A risk value calculation device according to claim 3. (Appendix 5) The storage unit stores the posture pattern associated with a predetermined pneumonia symptom. 5. A risk value calculation device according to claim 3 or 4. (Appendix 6) a congestion degree calculation means for calculating a congestion degree of a space from the visible light image data, the risk value calculation means calculates the infection risk value by further taking into account a degree of congestion of a predetermined space in which the person is included in the visible light image data. 6. A risk value calculation device according to any one of claims 1 to 5. (Appendix 7) The congestion degree calculation means calculates the congestion degree based on the number of people in a predetermined space. 7. A risk value calculation device according to claim 6. (Appendix 8) the risk value calculation means calculates the infection risk value of the person by further taking into account the body surface temperature and the symptoms of surrounding people around the person. 8. A risk value calculation device according to claim 6 or 7. (Appendix 9) the risk value calculation means calculates an infection risk value of the person calculated when the infection risk value of the surrounding person is a first risk value to be relatively higher than an infection risk value calculated when the infection risk value of the surrounding person is a second risk value lower than the first risk value; 9. A risk value calculation device according to claim 8. (Appendix 10) the risk value calculation means calculates the infection risk value of the person by further taking into account a distance between the person and surrounding people present around the person; 10. A risk value calculation device according to any one of claims 6 to 9. (Appendix 11) the risk value calculation means calculates an infection risk value of the person when the distance of the surrounding person is a first distance to be relatively higher than an infection risk value calculated when the infection risk value of the surrounding person is a second distance that is farther than the first distance; 11. A risk value calculation device according to claim 10. (Appendix 12) an authentication device that authenticates a person based on the visible light image data; and a risk value calculation device according to any one of claims 1 to 11, which calculates and outputs an infection risk value of the person related to the authentication. Risk value calculation system. (Appendix 13) The computer Obtaining visible light image data generated by a visible light camera and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data, Identifying a person included in the visible light image data and the thermal image data; Measure the body surface temperature of the identified person; detecting a symptom of an infectious disease in the person based on the visible light image data; Calculating an infection risk value for the person based on the body surface temperature and the symptoms; outputting the infection risk value; Risk value calculation method. (Appendix 14) A process of acquiring visible light image data generated by a visible light camera and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data; A process for identifying people included in the visible light image data and the thermal image data; A process of measuring a body surface temperature of the identified person; detecting a symptom of an infectious disease in the person based on the visible light image data; A process of calculating an infection risk value of the person based on the body surface temperature and the symptoms; A process of outputting the infection risk value; A non-transitory computer-readable medium storing a risk value calculation program for causing a computer to execute the above. [Explanation of symbols]

[0076] 11 Risk value calculation device 12 Risk value calculation device 13 Risk value calculation device 14 Risk value calculation device 21 User terminal 90 Facilities 91 Visible Light Camera 92 Thermal Camera 110 Image data acquisition unit 111 Temperature measurement section 112 Symptom detection unit 113 Risk Value Calculation Unit 114 Output section 115 Congestion degree calculation unit 120 Storage section 121 Symptom DB 122 Attribute information 200 Authentication Device 210 Face Features DB 220 Face detection unit 230 Feature Point Extraction Unit 240 Registration Department 250 Authentication Department 500 Network 700 Risk Value Calculation System

Claims

1. an image data acquisition means for acquiring visible light image data generated by a visible light camera and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data; a temperature measuring means for identifying a person included in the visible light image data and the thermal image data and measuring a body surface temperature of the identified person; a symptom detection means for detecting a symptom of an infectious disease of the person based on the visible light image data; a congestion degree calculation means for calculating a congestion degree of a predetermined space including the person from the visible light image data; a risk value calculation means for calculating an infection risk value of the person based on the body surface temperature, the symptoms, and the congestion level; and an output means for outputting the infection risk value. Risk value calculation device.

2. the symptom detection means detects the symptom from the posture of the person.

2. The risk value calculation device according to claim 1.

3. a memory unit configured to store a posture pattern associated with the symptom of the infectious disease; the symptom detection means detects the symptom by comparing the posture of the person with the posture pattern. The risk value calculation device according to claim 2 .

4. the symptom detection means detects the symptom when the posture of the person and the posture pattern match at least a predetermined rate within a predetermined period of time.

4. The risk value calculation device according to claim 3.

5. The storage unit stores the posture pattern associated with the predetermined symptom of pneumonia.

5. The risk value calculation device according to claim 3 or 4.

6. The congestion degree calculation means calculates the congestion degree based on the number of people in a predetermined space. The risk value calculation device according to any one of claims 1 to 5.

7. the risk value calculation means calculates the infection risk value of the person by further taking into account the body surface temperatures and the symptoms of surrounding people around the person. The risk value calculation device according to any one of claims 1 to 6.

8. The computer Obtaining visible light image data generated by a visible light camera and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data, Identifying a person included in the visible light image data and the thermal image data; Measure the body surface temperature of the identified person; detecting a symptom of an infectious disease in the person based on the visible light image data; calculating a congestion level of a predetermined space including the person based on the visible light image data; calculating an infection risk value for the person based on the body surface temperature, the symptoms, and the congestion level; outputting the infection risk value; Risk value calculation method.

9. A process of acquiring visible light image data generated by a visible light camera and thermal image data generated by an infrared camera having a shooting range corresponding to at least a part of the visible light image data; A process for identifying people included in the visible light image data and the thermal image data; A process of measuring a body surface temperature of the identified person; detecting a symptom of an infectious disease in the person based on the visible light image data; A process of calculating a congestion level of a predetermined space including the person based on the visible light image data; A process of calculating an infection risk value of the person based on the body surface temperature, the symptoms, and the congestion level; A process of outputting the infection risk value; A risk value calculation program that causes a computer to execute the above.

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