Patent Publication Number: US-2022237918-A1

Title: Monitoring camera and learning model setting support system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is based on and claims priority under 35 USC 119 from Japanese Patent Application No. 2021-008947 filed on Jan. 22, 2021, the contents of which are incorporated herein by reference. 
     TECHNICAL FIELD 
     The present disclosure relates to a monitoring camera and a learning model setting support system. 
     BACKGROUND ART 
     Patent Literature 1 discloses a monitoring camera including artificial intelligence. The monitoring camera receives a parameter related to a detection target from a terminal device, constructs artificial intelligence based on the parameter, and uses the constructed artificial intelligence to detect a detection target from an image captured by an imaging unit. 
     CITATION LIST 
     Patent Literature 
     Patent Literature 1: JP-2020-113945-A 
     SUMMARY OF INVENTION 
     In Patent Literature 1, a detection target is detected by switching a parameter (for example, an artificial intelligence (AI) learning model for detecting a detection target) set for each detection target. However, since a user who uses the monitoring camera may want to detect different objects or events, there is a demand for using different parameters that can be used in a monitoring camera depending on a time range, and it can be said that there is room for improvement in the usability of the monitoring camera. 
     An object of the present disclosure is to provide a monitoring camera and a learning model setting support system that can efficiently support the setting of a monitoring camera by a user and can improve usability during an operation of the monitoring camera. 
     The present disclosure provides a monitoring camera equipped with artificial intelligence. The monitoring camera includes an imaging unit configured to capture an image of a monitoring area, an acquisition unit configured to acquire schedule information indicating a time range in which at least one learning model used for the artificial intelligence that detects an object is validated, a detection unit configured to detect the object from an image captured by the imaging unit based on the learning model, and a processor configured to generate and output an alarm indicating that the object is detected when the object is detected by the detection unit. The processor is configured to switch the learning model based on the schedule information. 
     Further, the present disclosure provides a learning model setting support system including a terminal device configured to receive a user operation, and at least one monitoring camera that is configured to communicate with the terminal device and to capture an image of a monitoring area, the at least one monitoring camera being equipped with artificial intelligence. The terminal device is configured to generate schedule information indicating information about a time range in which at least one learning model used for the artificial intelligence that detects an object is validated based on the user operation, and to transmit the schedule information to the monitoring camera. The monitoring camera is configured to switch the learning model based on the schedule information, and to generate and output an alarm indicating that the object is detected when the object is detected. 
     According to the present disclosure, it is possible to efficiently support the setting of a monitoring camera by a user and improve usability during an operation of the monitoring camera. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram showing an example of an internal configuration of a learning model setting support system according to an embodiment. 
         FIG. 2  is a diagram showing an example of a schedule setting screen of a monitoring camera according to the embodiment. 
         FIG. 3  is a diagram showing an example of application switching of the monitoring camera. 
         FIG. 4  is a flowchart showing an example of an operation procedure of the monitoring camera according to the embodiment. 
         FIG. 5  is a diagram showing an example of an alarm screen. 
         FIG. 6  is a diagram showing an example of an alarm screen. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments specifically disclosing a monitoring camera and a learning model setting support system according to the present disclosure will be described in detail with reference to the accompanying drawings as appropriate. Unnecessarily detailed description may be omitted. For example, detailed description of a well-known matter or repeated description of substantially the same configuration may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding for those skilled in the art. The accompanying drawings and the following description are provided for those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter described in the claims. 
     An internal configuration of a learning model setting support system  100  according to an embodiment will be described with reference to  FIG. 1 .  FIG. 1  is a diagram showing an example of an internal configuration of the learning model setting support system  100  according to the embodiment. 
     The learning model setting support system  100  is a system that can switch an application for detecting a detection target from a monitoring area monitored by at least one monitoring camera C 1  according to a day of the week, a time range, or the like. When a user who possesses or uses each of the monitoring cameras C 1  operates the terminal device P 1 , the learning model setting support system  100  receives and sets a schedule setting of an application that detects a detection target for each monitoring camera. The learning model setting support system  100  includes one or more monitoring cameras C 1 , the terminal device P 1 , a network NW, and an external storage medium M 1 . Although only one external storage medium M 1  is shown in the example shown in  FIG. 1 , the learning model setting support system  100  may include a plurality of external storage media M 1 . 
     Each of the monitoring cameras C 1  in the learning model setting support system  100  is a camera equipped with artificial intelligence (AI), analyzes a captured video (captured image) using an AI learned model, and detects a specific detection target or detection object set by a user. Each of the monitoring cameras C 1  is connected to the terminal device P 1  via the network NW so that the monitoring camera C 1  can execute data communication with the terminal device P 1 , and the monitoring camera C 1  executes an image processing on an image captured by an imaging unit  13  based on various types of setting information that is used for detecting a detection target and is transmitted from the terminal device P 1 , and detects a detection target. 
     The various types of setting information referred to here are, for example, information about detection settings such as a detection target (for example, a person, a two-wheel vehicle, a vehicle, and the like), a detection area or a detection line where a detection target is detected, and a detection mode for detecting a detection target in each detection area or each detection line (for example, an intrusion detection of detecting an object that intrudes into a detection area, a stay detection, a direction detection, a line cross detection, and the like), an application for detecting a detection target set in the detection setting, a schedule for validating an application used (validated) in each of the monitoring cameras C 1 . The detection setting at least includes information about at least one application to be validated. It is needless to say that the various types of setting information are not limited to those described above. 
     Each of the monitoring cameras C 1  includes a communication unit  10 , a processor  11 , a memory  12 , the imaging unit  13 , an AI processing unit  14 , an external storage medium interface (I/F)  15 , and a registration database DB. Although an example is shown in which the registration database DB shown in  FIG. 1  is integrally formed with each of the monitoring cameras C 1 , the registration database DB may be formed separately, or may be formed separately and connected to each of a plurality of monitoring cameras C 1  so that the registration database DB can execute data communication with each of the plurality of monitoring cameras C 1 . 
     The communication unit  10  serving as an example of an acquisition unit is connected to the terminal device P 1  via the network NW so that the communication unit  10  can execute data communication with the terminal device P 1 . The communication unit  10  may be connected to the terminal device P 1  so that the communication unit  10  can execute wired communication, or may be connected to the terminal device P 1  via a wireless network such as a wireless LAN. The wireless communication referred to here is, for example, short-range wireless communication such as Bluetooth (registered trademark) or NFC (registered trademark), or communication via a wireless local area network (LAN) such as Wi-Fi (registered trademark). 
     The communication unit  10  transmits an alarm that is generated by the AI processing unit  14  and indicates that a detection target is detected to the terminal device P 1  via the network NW. The communication unit  10  acquires various types of setting information transmitted from the terminal device P 1  via the network NW, and outputs the acquired various types of setting information to the processor  11 . 
     The processor  11  is configured with, for example, a central processing unit (CPU) or a field programmable gate array (FPGA), and executes various processings and controls in cooperation with the memory  12 . Specifically, the processor  11  achieves a function of each unit by referring to a program and data stored in the memory  12  and executing the program. The function referred to here includes, for example, a function of executing an image processing on a captured image based on various types of setting information, a function of switching an application used by the AI processing unit  14  based on schedule information, a function of generating an alarm for notifying that a detection target is detected based on detection data detected by the AI processing unit  14 , and the like. 
     The processor  11  executes an image processing on an image captured by the imaging unit  13  based on various types of setting information transmitted from the terminal device P 1 , and outputs a result to the AI processing unit  14 . When a control signal or detection data indicating that a detection target is detected is output from the AI processing unit  14 , the processor  11  generates an alarm for notifying that a detection target is detected based on the control signal or the detection data. The processor  11  transmits the alarm to the terminal device P 1  via the communication unit  10 . 
     The memory  12  includes, for example, a random access memory (RAM) serving as a work memory used when each processing of the processor  11  is executed, and a read only memory (ROM) that stores a program and data for defining an operation of the processor  11 . The RAM temporarily stores data or information generated or acquired by the processor  11 . A program that defines an operation of the processor  11  is written into the ROM. The memory  12  stores an image captured by the imaging unit  13 , various types of setting information transmitted from the terminal device P 1 , and the like. 
     The imaging unit  13  includes at least a lens (not shown) and an image sensor (not shown). The image sensor is, for example, a solid-state imaging device such as a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS), and converts an optical image formed on an imaging surface into an electric signal. The imaging unit  13  outputs the captured image to the processor  11 . 
     The AI processing unit  14  is configured with, for example, a CPU, a digital signal processor (DSP), or an FPGA, and switches an application to be validated based on schedule information. The AI processing unit  14  executes an image processing and an analysis processing on an image captured by the imaging unit  13  based on learning data corresponding to the validated application. The AI processing unit  14  includes an AI calculation processing unit  14 A, a decoding processing unit  14 B, and a learning model database  14 C. 
     The AI calculation processing unit  14 A serving as an example of a detection unit executes an image processing and an analysis processing on an image captured by the imaging unit  13  based on various types of setting information output from the processor  11  and an application (a learning model) that is stored in the learning model database  14 C and is validated by the processor  11 . When it is determined that a detection target is detected, the AI calculation processing unit  14 A generates detection data related to the detected detection target (for example, a face image or simplified recorded video data of a detected person, a captured image on which a detection frame indicating a position of a detection target is superimposed, and the like). The AI calculation processing unit  14 A outputs the generated detection data to the processor  11 . 
     When learning data is transmitted from the terminal device P 1 , the decoding processing unit  14 B decodes the learning data output from the processor  11 . The decoding processing unit  14 B outputs to and stores the decoded learning data in the learning model database  14 C. 
     The learning model database  14 C includes a storage device including one of a semiconductor memory such as a RAM and a ROM and a storage device such as a solid state drive (SSD) or a hard disk drive (HDD). The learning model database  14 C generates or stores, for example, a program for defining an image processing to be executed by the AI calculation processing unit  14 A and various applications (that is, learning models) used for a detection processing of a detection target executed by the AI calculation processing unit  14 A. 
     The various applications referred to here include a face authentication application  141 A that detects each of a plurality of persons registered in the registration database DB by a face authentication processing, a suspicious behavior detection application  141 B that detects a predetermined behavior (for example, a behavior that may trigger an incident such as dizziness of a person, quarrel, possession of a pistol, and shoplifting) performed by a person, a moving body detection application  141 C that detects a moving body, and the like. The various applications described above are merely examples, and the present invention is not limited thereto. The learning model database  14 C may store, for example, an application for detecting a color, a vehicle type, a license plate, or the like of a two-wheel vehicle or a vehicle as another application. The predetermined behavior detected by the suspicious behavior detection application  141 B is not limited to those described above. 
     Learning of generating learning data may be executed using one or more statistical classification techniques. The statistical classification technique includes, for example, linear classifiers, support vector machines, quadratic classifiers, kernel estimation, decision trees, artificial neural networks, Bayesian technologies and/or networks, hidden Markov models, binary classifiers, multi-class classifiers, a clustering technique, a random forest technique, a logistic regression technique, a liner regression technique, a gradient boosting technique, and the like. The statistical classification technique is not limited thereto. The generation of learning data may be executed by the AI processing unit  14  of each of the monitoring cameras C 1 , or may be executed by, for example, the terminal device P 1  that is communicably connected to the monitoring camera C 1  using the network NW. Furthermore, the learning data may be received (acquired) from the terminal device P 1  via the network NW, or may be received (acquired) from the external storage medium M 1  that is communicably connected via the external storage medium I/F  15 . 
     The external storage medium OF  15  is provided such that the external storage medium M 1  (for example, a universal serial bus (USB) memory, a secure digital memory (SD) (registered trademark) card, and the like) can be inserted into and removed from the external storage medium I/F  15 , and is connected to the external storage medium M 1  such that the external storage medium I/F  15  can execute data communication with the external storage medium M 1 . The external storage medium I/F  15  acquires learning data stored in the external storage medium M 1  based on a request from the processor  11 , and outputs the learning data to the processor  11 . The external storage medium OF  15  transmits data of an image (a video) captured by the imaging unit  13 , learning data generated by the AI processing unit  14  of each of the monitoring cameras C 1 , and the like to the external storage medium M 1  and stores the data in the external storage medium M 1 , based on a request from the processor  11 . The external storage medium I/F  15  may be connected to a plurality of external storage media so that the external storage medium OF  15  can execute data communication with the plurality of external storage media at the same time. 
     The external storage medium M 1  is, for example, a storage medium such as a USB memory or an SD (registered trademark) card, and stores an image (a video) captured by the imaging unit  13 . The external storage medium M 1  may store learning data or the like generated by another monitoring camera or the terminal device P 1 . 
     The registration database DB is configured with a storage device including any one of a semiconductor memory such as a RAM and a ROM and a storage device such as an SSD or an HDD. The registration database DB registers (stores) detection target information related to a specific detection target, a captured image of a specific detection target (for example, a face image of a person to be detected, a captured image of a two-wheel vehicle or a vehicle, an image of a license plate, or the like), captured video data of a specific detection target, detection history information in which a specific detection target was detected in the past, and the like. The registration database DB may be configured separately from the plurality of monitoring cameras C 1 , or may be configured separately from the plurality of monitoring cameras C 1  and connected to the plurality of monitoring cameras C 1  so that the registration database DB can execute data communication with the plurality of cameras C 1 . 
     Although an example is described in the embodiment in which the registration database DB stores (registers) face images of a plurality of persons, the registration database DB is not limited thereto. The registration database DB registers (stores) a captured image or a recorded video used in a detection processing or an authentication processing of a specific detection target (that is, a detection target such as a person, a two-wheel vehicle, and a vehicle designated by a user) detected by an application provided in the AI processing unit  14 . For example, the data registered (stored) in the registration database DB may be a captured image of a two-wheel vehicle or a vehicle, a captured image of a license plate of a two-wheel vehicle or a vehicle, a whole body image of a person, or the like. Further, the registration database DB may register (store) information indicating a feature or the like of a detection target (for example, attribute information (gender, height, physique, and the like) of a person, license plate information of a two-wheel vehicle or a vehicle, a vehicle type of a two-wheel vehicle or a vehicle, color information of a two-wheel vehicle or a vehicle, and the like), a past detection history (an alarm history), and the like in association with the registered (stored) captured image or recorded video. 
     The terminal device P 1  is, for example, a device such as a personal computer (PC), a tablet, and a smartphone, and includes an interface (for example, a keyboard, a mouse, or a touch panel display) that can receive an input operation (a user operation) of a user. The terminal device P 1  is connected to each of the monitoring cameras C 1  via the network NW so that the terminal device P 1  can execute data communication with the monitoring cameras C 1 , and transmits a signal (for example, various types of setting information, learning data, an application, or the like) generated based on a user operation to the monitoring cameras C 1  via the network NW. The terminal device P 1  generates an alarm screen (for example, alarm screens SC 2  and SC 3  shown in  FIGS. 5 and 6 ) based on a captured image transmitted from each of the monitoring cameras C 1  via the network NW or an alarm transmitted from each of the monitoring cameras C 1 , and displays the alarm screen on a monitor (not shown). 
     The network NW is communicably connected to the terminal device P 1  and each of the monitoring cameras C 1  via a wireless communication network or a wired communication network. The wireless communication network referred to here is provided in accordance with a wireless communication standard such as a wireless LAN, a wireless WAN, a fourth generation mobile communication system (4G), a fifth generation mobile communication system (5G), or Wi-Fi (registered trademark). 
     Next, a schedule setting screen SC 1  set for each of the monitoring cameras C 1  will be described with reference to  FIG. 2 .  FIG. 2  is a diagram showing an example of the schedule setting screen SC 1  of the monitoring camera C 1  according to the embodiment. The schedule setting screen SC 1  shown in  FIG. 2  is merely an example, and it is needless to say that the present invention is not limited thereto. 
     The schedule setting screen SC 1  is generated by the terminal device P 1 , and is output and displayed on a monitor (not shown) provided in the terminal device P 1 . The schedule setting screen SiC receives a setting operation of a schedule of each of the monitoring cameras C 1  from a user who operates the terminal device P 1 . 
     The schedule setting screen SC 1  includes a time table list TT, detailed setting fields TBA 1  and TBB 1  of one or more time tables TBA and TBB set in the time table list TT, and a setting button BT 0 . Although an example of the schedule setting screen SC 1  shown in  FIG. 2  is described in which two timetables TBA and TBB are set for each of the one or more monitoring cameras C 1 , the number of time tables to be set is not limited thereto, and the number of time tables may be at least one or more. The schedule set on the schedule setting screen SC 1  is set and applied to one or more monitoring cameras designated by a user operation. 
     The time table list TT includes one or more time tables TBA and TBB set for each of the one or more monitoring cameras C 1  designated by a user operation, and an Off table TBC for setting a detection function implemented by each of the monitoring cameras C 1  to OFF. The time table list TT receives, from a user, a designation operation of a day of the week for applying a time table (specifically, the time tables TBA, TBB, and the Off table TBC) to each of the one or more monitoring cameras C 1 . For example, in the time table list TT in the example shown in  FIG. 2 , the day of the week “Monday, Tuesday, Wednesday, Thursday, Friday” is set in the time table TBA and the day of the week “Saturday, Sunday” is set in the time table TBB for each of the monitoring cameras C 1 . 
     The detailed setting fields TBA 1  and TBB 1  receive a time range designation operation from a user for setting a time range in which a detection setting is set and for setting all applications of the one or more monitoring cameras  1  to an invalidated state (that is, an off state) in the time tables TBA and TBB set in the time table list TT. The detailed setting fields TBA 1  and TBB 1  respectively include time range designation fields TBA 11  and TBB 11 , detection setting validating time range fields TBA 12  and TBB 12 , and detection setting designation fields TBA 13  and TBB 13 . 
     The time range designation fields TBA 11  and TBB 11  receive a designation operation from a user for designating a time range in which each detection setting designated in the detection setting designation fields TBA 13  and TBB 13  is validated. 
     The detection setting validating time range fields TBA 12  and TBB 12  visualize each time range designated in the time range designation fields TBA 11  and TBB 11 . The detection setting validating time range fields TBA 12  and TBB 12  indicate that the respective detection settings designated in the detection setting designation fields TBA 13  and TBB 13  by a user operation are validated in the respective time ranges designated in the time range designation fields TBA 11  and TBB 11 . 
     The detection setting designation fields TBA 13  and TBB 13  receive a designation operation from a user for designating a detection setting validated in each of the monitoring cameras C 1  in each time range designated in the time range designation fields TBA 11  and TBB 11 . 
     For example, in the example shown in  FIG. 2 , the time table TBA indicated by a “time table  1 ” indicates a schedule for validating a detection setting (an application) indicated by operation content “detection setting  1 ” in a time range “9:00 to 12:00”, validating a detection setting (an application) indicated by operation content “detection setting  2 ” in a time range “12:00 to 21:00”, and validating a detection setting (an application) indicated by operation content “detection setting  3 ” in a time range “21:00 to 9:00”. The time table TBB indicated by a “time table  2 ” indicates a schedule for validating a detection setting (an application) indicated by operation content “detection setting  3 ” in a time range “00:00 to 24:00”. 
     The setting button BT 0  is a button for setting application schedules that are validated in the time table list TT and one or more time tables TBA and TBB set by a user operation in a predetermined monitoring camera. When the setting button BT 0  is selected (pressed) by a user operation, the terminal device P 1  generates schedule information based on each time range and detection setting designated in the time table list TT and the one or more time tables TBA and TBB, transmits the schedule information to a monitoring camera designated by a user, and causes the monitoring camera to set the schedule information. 
     Here, a specific example of application switching of the monitoring camera C 1  based on the schedule set on the schedule setting screen SC 1  shown in  FIG. 2  will be described with reference to  FIG. 3 .  FIG. 3  is a diagram showing an example of application switching of the monitoring camera C 1 . An installation location of the monitoring camera C 1 , a monitoring area, and a time table set in the monitoring camera C 1  to be described below are merely examples, and it is needless to say that the present invention is not limited thereto. The detection setting to be described below is merely an example, and the present invention is not limited thereto. 
     The monitoring camera C 1  shown in  FIG. 3  is, for example, a monitoring camera that captures an image of a doorway of a store, and a schedule set in the “time table  1 ” set on the schedule setting screen SC 1  is set in the monitoring camera C 1 . 
     In the example shown in  FIG. 3 , the “detection setting  1 ” is a setting in which the face authentication application  141 A and the suspicious behavior detection application  141 B are validated at the same time in the time range “9:00 to 12:00”. The “detection setting  2 ” is a setting in which the face authentication application  141 A is validated in the time range “12:00 to 21:00”. The “detection setting  3 ” is a setting in which the moving body detection application  141 C is validated in the time range “21:00 to 9:00”. 
     When it is determined that the current time is “9:00”, the monitoring camera C 1  executes an application switching processing of validating the face authentication application  141 A and the suspicious behavior detection application  141 B at the same time based on the “time table  1 ”. When it is determined that the current time is “12:00”, the monitoring camera C 1  executes an application switching processing of validating the face authentication application  141 A based on the “time table  1 ”. When it is determined that the current time is “21:00”, the monitoring camera C 1  executes an application switching processing of validating the moving body detection application  141 C based on the “time table  1 ”. 
     By setting such a schedule, for example, a user can detect a person who is prohibited from entering and leaving a store using the monitoring camera C 1  or can detect whether there is a predetermined behavior (for example, shoplifting, pickpocketing, and the like), for each of a plurality of persons who visit the vicinity of the doorway of the store in the time range “9:00 to 12:00” of a time sale set in the vicinity of the doorway of the store where the monitoring camera C 1  is installed. For example, a user can detect a person who is prohibited from entering and leaving the store among a plurality of persons who visit the store using the monitoring camera C 1  during the time range “12:00 to 21:00” from when a time sale set in the vicinity of the doorway of the store where the monitoring camera C 1  is installed up to when the store is closed, or can detect a person who is about to enter the store during the time range “21:00 to 9:00” from when the store is closed to when the store is opened in the next morning. That is, for example, a user can automatically switch an application used in a detection processing executed by the monitoring camera and an application setting by setting a schedule based on a detection target, a detection condition, or the like desired to be detected by each monitoring camera. 
     As described above, the learning model setting support system  100  according to the embodiment can support the setting of each of the monitoring cameras C 1  by a user on the schedule setting screen SC 1 , and can improve usability during an operation of each of the monitoring cameras C 1 . 
     Next, an operation procedure of the monitoring camera C 1  according to the embodiment will be described with reference to  FIG. 4 .  FIG. 4  is a flowchart showing an example of an operation procedure of the monitoring camera C 1  according to the embodiment. 
     Each of the monitoring cameras C 1  executes switching of an application to be validated based on schedule information transmitted from the terminal device P 1  in advance or at any timing. 
     Each of the monitoring cameras C 1  counts the current time (St 1 ) and refers to the schedule information stored in the memory  12  (St 2 ). Each of the monitoring cameras C 1  determines whether there is an application to be validated (that is, whether there is a set schedule) at the counted current time based on the schedule information (St 3 ). 
     When it is determined that there is an application to be validated (that is, there is a set schedule) at the counted current time (St 3 , YES), each of the monitoring cameras C 1  switches to the application based on the schedule information (St 4 ), and starts a detection processing on a detection target based on the application. 
     In the processing in step St 4 , when the schedule information is information indicating that a detection target detection function to be executed by a monitoring camera is turned off (that is, all applications are invalidated), each of the monitoring cameras C 1  invalidates all applications and returns to the processing in step St 1 . 
     On the other hand, when it is determined that there is no application to be validated (that is, there is no set schedule) at the counted current time (St 3 , NO), each of the monitoring cameras C 1  continues the detection processing on the detection target based on the application that is currently validated. 
     Next, the alarm screen SC 2  will be described with reference to  FIG. 5 .  FIG. 5  is a diagram showing an example of the alarm screen SC 2  displayed on the terminal device P 1 . The alarm screen SC 2  shown in  FIG. 5  is merely an example, and the present invention is not limited thereto. The alarm screen SC 2  generated when the face authentication application  141 A is validated will be described in the example shown in  FIG. 5 . 
     The alarm screen SC 2  is generated by the terminal device P 1  based on an alarm transmitted from each of the monitoring cameras C 1 , and is displayed on a monitor (not shown) of the terminal device P 1 . For example, the alarm screen SC 2  shown in  FIG. 5  includes a captured image or a recorded video of a person registered in the registration database DB of each of the monitoring cameras C 1  in advance as a detection target and a captured image (a face image in the example shown in  FIG. 5 ) or a recorded video of a person captured by each of the monitoring cameras C 1 , and the person registered as the detection target and the detected person are displayed in a manner in which the person registered as the detection target and the detection person can be compared with each other. 
     The alarm screen SC 2  includes a detection history field HS, a registered face image RGF, a face image DFT, thumbnail images RGV and DTV, video playback buttons BT 1  and BT 2 , a true alarm determination button BT 3 , a video download button BT 4 , a mail notification button BT 5 , detection information MS 1 , and registration information MS 2 . 
     The detection history field HS displays detection date and time information indicating when a person is detected corresponding to each alarm, and monitoring camera information of a monitoring camera that detects a person (for example, area information monitored (captured) by a monitoring camera, a monitoring camera name, an identification number assigned to each monitoring camera, and the like), as detection history information of each alarm transmitted from each of the plurality of monitoring cameras C 1 . For example, in the detection history field HS shown in  FIG. 5 , six pieces of detection history information corresponding to respective pieces of alarm notification information are displayed as “12:34:56, ∘store, doorway”, “12:00:00, ∘store, doorway”, “11:00:00, ∘store, doorway”, “10:00:00, ∘store, doorway”, “9:00:00, ∘store, doorway”, and “8:00:00, ∘store, doorway”. In the detection history field HS shown in  FIG. 5 , the detection history information corresponding to the latest alarm notification information is displayed on an upper side of the drawing, this is merely an example, and the present invention is not limited thereto. 
     When a user selects (presses) any one of at least one piece of the detection history information displayed in the detection history field HS, the terminal device P 1  generates and displays an alarm screen based on an alarm corresponding to the selected (pressed) detection history information. The terminal device P 1  highlights the detection history information corresponding to the alarm screen currently displayed on the monitor by a change such as a bold character, a frame line, a character color, and the like. When a clear button BT 6  is selected (pressed) by a user operation in a state in which one or more pieces of detection history information in the detection history field HS are selected and operated, the terminal device P 1  deletes the detection history information displayed in the detection history field HS. 
     The face image DFT is a face image of a person captured by each of the plurality of monitoring cameras C 1 . The registered face image RGF is a face image of a person who is determined to be the same as or similar to a person corresponding to a face image DFT that is registered (stored) in the registration database DB in advance and is captured by each of the plurality of monitoring cameras C 1 . 
     The thumbnail image RGV is a thumbnail image of recorded video data of a person of the registered face image RGF that is registered (stored) in the registration database DB in advance. A playback button BTR is superimposed on the thumbnail image RGV. When the video playback button BT 1  or the playback button BTR is selected (pressed) by a user operation, the terminal device P 1  plays back the recorded video data. 
     The thumbnail image DTV is a thumbnail image of recorded video data corresponding to the face image DFT that is registered (stored) in the registration database DB in advance and is captured by a monitoring camera. A playback button BTD is superimposed on the thumbnail image DTV. When the video playback button BT 2  or the playback button BTD is selected (pressed) by a user operation, the terminal device P 1  plays back the recorded video data. 
     The detection information MS 1  includes monitoring camera information “0 store, store doorway camera” of a monitoring camera that captures the face image DFT and imaging date and time information “12:34:56 January 5, 2021”. 
     The registration information MS 2  includes category information “category: shoplifter” of a person corresponding to the registered face image RGF registered (stored) in the registration database DB, imaging date and time information “August 1, 2020” when the registered face image RGF is captured, monitoring camera information “0 store, car supplies” of a monitoring camera, and attribute information “40-year-old, male, 180 cm” and feature information “athletic physique” of a person corresponding to the registered face image RGF registered (stored) in the registration database DB. The registration information MS 2  shown in  FIG. 5  is merely an example, and the present invention is not limited thereto. For example, the registration information MS 2  may only include the imaging date and time information when the registered face image RGF is captured and the monitoring camera information. 
     The true alarm determination button BT 3  is a button for requesting a monitoring camera to generate a video file of a person corresponding to an alarm. When a user determines that an alarm corresponding to the alarm screen SC 2  is not a false alarm by visually checking the registered face image RGF, the face image DFT, the recorded video data indicated by the thumbnail image RGV, or the recorded video data indicated by the thumbnail image DTV displayed on the alarm screen SC 2 , the user selects (presses) the true alarm determination button BT 3 . When the user selects (presses) the true alarm determination button BT 3 , the terminal device P 1  generates a control command for requesting generation of a video file corresponding to the recorded video data, and transmits the control command to a monitoring camera that is a transmission source of the alarm. Each of the plurality of monitoring cameras C 1  generates a video file based on the control command transmitted from the terminal device P 1 , and then transmits the video file or a URL of the video file to the terminal device P 1 . 
     The video download button BT 4  is a button for downloading the video file generated by each of the plurality of monitoring cameras C 1  into a memory (not shown) of the terminal device P 1 . When a user selects (presses) the video download button BT 4 , the terminal device P 1  generates and transmits a control command for requesting transmission of the generated video file. The terminal device P 1  downloads the video file transmitted from each of the plurality of monitoring cameras C 1 . 
     The mail notification button BT 5  is a button for transmitting to, a terminal (for example, a smartphone, a tablet terminal, a PC, or the like) registered in advance by a user, a notification mail indicating that an alarm is issued. When a user selects (presses) the mail notification button BT 5 , the terminal device P 1  generates a notification mail including the registered face image RGF, the face image DFT, registered simplified video data indicated by the thumbnail image RGV, simplified recorded video data indicated by the thumbnail image DTV, the detection information MS 1 , and the registration information MS 2 , and transmits the notification mail to at least one terminal registered in advance. 
     When the generation of the video file is completed, the terminal device P 1  generates a notification mail including a URL of the video file, or when text data of a feature (for example, clothes, a body shape, an accessory, belongings, or the like) of a person generated by a user operation and text data of a location (for example, position information, area information, and the like of a monitoring camera) of a monitoring camera that finally images (detects) the person are generated, the terminal device P 1  generates a notification mail including the generated text data. 
     Next, the alarm screen SC 3  will be described with reference to  FIG. 6 .  FIG. 6  is a diagram showing an example of the alarm screen SC 3  displayed on the terminal device P 1 . The alarm screen SC 3  shown in  FIG. 6  is merely an example, and the present invention is not limited thereto. The alarm screen SC 3  generated when the suspicious behavior detection application  141 B is validated will be described in the example shown in  FIG. 6 . 
     The alarm screen SC 3  is generated by the terminal device P 1  based on an alarm transmitted from each of the monitoring cameras C 1 , and is displayed on a monitor (not shown) of the terminal device P 1 . For example, the alarm screen SC 3  shown in  FIG. 6  includes a live video in which a detection frame FR indicating a place where a predetermined behavior is detected is superimposed on a captured video (live video) captured by each of the monitoring cameras C 1 . The live video may be a detection image in which a detection frame indicating a place where a predetermined behavior is detected is superimposed on a captured image captured by each of the monitoring cameras C 1 . 
     The alarm screen SC 3  includes a live image field STL 1 , a setting field STL 2 , a live video display area SC 31 , and a detection alarm field ARL. 
     The live image field STL 1  includes various items that can be set related to the display of a live video of a monitoring camera displayed in the current live video display area SC 31 . The live image field STL 1  includes monitoring camera information LT 1 , a zoom adjustment field LT 2 , a brightness adjustment field LT 3 , and a log display and playback button BT 9 . 
     The monitoring camera information LT 1  indicates information related to a monitoring camera that captures the live video displayed in the live video display area SC 31 . For example, the monitoring camera information LT 1  in the example shown in  FIG. 6  indicates resolution information “resolution: 2560×1440” of the monitoring camera that captures the live video displayed in the live video display area SC 31 , imaging mode information “mode: frame rate designation” set in the monitoring camera, FPS information “FPS: 30 fps”, image quality setting information “image quality: normal”, communication speed information “speed: 6144 kbps”, and the like. 
     The zoom adjustment field LT 2  enlarges or reduces the live video displayed in the live video display area SC 31  based on a user operation. When any one of various magnifications (×1, ×2, and ×4 in the example shown in  FIG. 6 ) shown in the zoom adjustment field LT 2  is selected (pressed) by a user operation, the terminal device P 1  displays the live video displayed in the live video display area SC 31  by enlarging or reducing the live video to the selected magnification. The various magnifications shown in  FIG. 6  are merely examples, and the present invention is not limited thereto. 
     The brightness adjustment field LT 3  includes a button BT 7  for brightening the brightness of the live video displayed in the live video display area SC 31  and a button BT 8  for darkening the brightness of the live video. The terminal device P 1  displays the live video by adjusting (changing) the brightness of the live video displayed in the live video display area SC 31  based on a selection (pressing) operation of the button BT 7  or the button BT 8  by a user. 
     The log display and playback button BT 9  is a button for displaying or playing back a recorded video recorded in a recorder RD. When the log display and playback button BT 9  is selected (pressed) by a user operation, the terminal device P 1  displays or plays back a recorded video that is captured by a monitoring camera that captures an image of the live video display area SC 31  and is recorded in the recorder RD. 
     When the setting field STL 2  is selected by a user operation, the setting field STL 2  displays a screen (for example, the schedule setting screen SC 1  shown in  FIG. 2 ) that can receive various settings such as the setting of a detection area in which a detection processing is executed by a validated application in each of the monitoring cameras C 1 , setting of a detection mode detected in each detection area, setting of a detection target (for example, a person, a two-wheel vehicle, a vehicle, or the like), and a schedule setting of each of the monitoring cameras C 1 . The terminal device P 1  stores various types of setting information set by a user operation in a memory (not shown) and transmits the setting information to a monitoring camera designated by a user. 
     The live video display area SC 31  displays a live video of a monitoring camera designated by a user operation. When the AI processing unit  14  detects a detection target, the terminal device P 1  displays a live video in which the detection frame FR indicating the detection target is superimposed on the live video. Here, the live video display area SC 31  shown in  FIG. 6  indicates an example of detecting shoplifting by the suspicious behavior detection application  141 B as a predetermined behavior and indicates an example of displaying a live image in which the detection frame FR is superimposed on a portion (area) detected as the predetermined behavior. The detection frame FR may be superimposed by each of the monitoring cameras C 1 , transmitted to the terminal device P 1 , and displayed on a monitor (not shown) of the terminal device P 1 . 
     The detection alarm field ARL notifies that a detection target is detected by a currently validated application and a detection mode set in the application. The detection alarm field ARL includes, for example, an alarm icon AR 1 , a line cross icon AR 2 , an intrusion detection icon AR 3 , a stay detection icon AR 4 , a direction detection icon AR 5 , and the like as one or more icons corresponding to each detection mode. 
     The alarm icon AR 1  is turned on when a detection target is detected in at least one detection mode. When the alarm icon AR 1  is selected (pressed) by a user operation in a state where the alarm icon AR 1  is turned on, the terminal device P 1  cancels alarm states (turn-on states) of all icons in a turn-on state in the detection alarm field ARL and turns off the icons. 
     The line cross icon AR 2  is an icon for notifying that a detection target (for example, a person, a two-wheel vehicle, a vehicle, or the like) passes through a predetermined detection line in a designated direction designated by a user. When it is determined that the detection target entered the predetermined detection line, each of the monitoring cameras C 1  generates an alarm including information about a detection mode (here, line cross) and transmits the alarm to the terminal device P 1 . The terminal device P 1  acquires the alarm transmitted from each of the monitoring cameras C 1 , and turns on the line cross icon AR 2  corresponding to the alarm. 
     The intrusion detection icon AR 3  is an icon for notifying that a detection target entered a predetermined detection area set by a user. When it is determined that the detection target entered the predetermined detection area, each of the monitoring cameras C 1  generates an alarm including information about a detection mode (here, an intrusion detection) and transmits the alarm to the terminal device P 1 . The terminal device P 1  acquires the alarm transmitted from each of the monitoring cameras C 1 , and turns on the intrusion detection icon AR 3  corresponding to the alarm. 
     The stay detection icon AR 4  is an icon for notifying that a detection target stays in a predetermined detection area set by a user for a certain period of time (for example, 30 seconds, 1 minute, or the like) or more. When it is determined that the detection target stays in the predetermined detection area for a certain period of time or more, each of the monitoring cameras C 1  generates an alarm including information about a detection mode (here, a stay detection) and transmits the alarm to the terminal device P 1 . The terminal device P 1  acquires the alarm transmitted from each of the monitoring cameras C 1 , and turns on the stay detection icon AR 4  corresponding to the alarm. 
     The direction detection icon AR 5  is an icon for notifying that a detection target moved in a predetermined designated direction in a predetermined detection area set by a user. When it is determined that the detection target moved in the designated direction in the predetermined detection area, each of the monitoring cameras C 1  generates an alarm including information about a detection mode (here, a direction detection) and transmits the alarm to the terminal device P 1 . The terminal device P 1  acquires the alarm transmitted from each of the monitoring cameras C 1 , and turns on the direction detection icon AR 5  corresponding to the alarm. 
     As described above, each of the monitoring cameras C 1  according to the embodiment is a monitoring camera equipped with artificial intelligence. Each of the monitoring cameras C 1  includes the imaging unit  13  that captures an image of a monitoring area, the communication unit  10  (an example of an acquisition unit) that acquires schedule information indicating a time range in which at least one learning model (application) used for the artificial intelligence that detects a detection target (an example of an object) is validated, the AI calculation processing unit  14 A (an example of a detection unit) that detects a detection target from an image captured by the imaging unit  13  based on the learning model (application), and the processor  11  that generates and outputs an alarm indicating that the detection target is detected when the detection target is detected by the AI calculation processing unit  14 A. The processor  11  switches a learning model (an application) based on the schedule information. 
     Accordingly, each of the monitoring cameras C 1  according to the embodiment can set and switch a learning model (an application) validated to detect a detection target according to a time range. Therefore, each of the monitoring cameras C 1  can improve usability during an operation of the monitoring camera. 
     As described above, the schedule information acquired by each of the monitoring cameras C 1  according to the embodiment is a schedule for validating a moving body detection application (an example of a moving body detection learning model) that detects a moving body to be detected in a time range of nighttime and validating a face authentication application (an example of a face authentication learning model) that detects a face of a person to be detected in a time range of daytime and that determines whether the detected face of the person matches or is similar to a face image of a person registered in advance. Accordingly, each of monitoring cameras C 1  according to the embodiment can switch an application (a learning model) that detects a detection target based on whether the current time is nighttime or daytime, and can detect a moving body in a monitoring area during nighttime and detect a person the same as or similar to each face image of a person registered (stored) in the registration database DB during daytime. 
     As described above, the schedule information acquired by each of the monitoring cameras C 1  according to the embodiment is a schedule for validating the face authentication learning application and a suspicious behavior detection application (an example of a suspicious behavior detection learning model) that detects a behavior of a person to be detected during a time range of daytime and determines whether the detected behavior of the person is a predetermined behavior registered in advance. Accordingly, for example, each of the monitoring cameras C 1  according to the embodiment can execute a plurality of applications at the same time during a time range of daytime, and can execute, at the same time, a detection processing of a person the same as or similar to each face image of a person registered (stored) in the registration database DB and a detection processing of a predetermined behavior (for example, a behavior that may trigger an incident such as dizziness of a person, quarrel, possession of a pistol, and shoplifting) performed by each of a plurality of persons that are captured. 
     As described above, the schedule information acquired by each of the monitoring cameras C 1  according to the embodiment is a schedule for validating a moving body detection application (an example of a moving body detection learning model) that detects a moving body to be detected in a time range of nighttime, and validating a suspicious behavior detection application (an example of a suspicious behavior detection learning model) that detects a behavior of a person to be detected in a time range of daytime and that determines whether the detected behavior of the person is a predetermined behavior registered in advance. Accordingly, each of the monitoring cameras C 1  according to the embodiment can switch an application that detects a detection target based on whether the current time is nighttime or daytime in each of the monitoring cameras C 1 , can detect a moving body in a monitoring area during nighttime, and can detect a predetermined behavior (for example, a behavior that may trigger an incident such as dizziness of a person, quarrel, possession of a pistol, and shoplifting) performed by each of a plurality of persons that are captured during daytime. 
     As described above, the schedule information acquired by each of the monitoring cameras C 1  according to the embodiment is a schedule for validating the suspicious behavior detection application and a face authentication application (an example of a face authentication learning model) that detects a face of a person to be detected and determines whether the detected face of the person matches or is similar to a face image of a person registered in advance in a time range of daytime. Accordingly, for example, each of the monitoring cameras C 1  according to the embodiment can execute a plurality of applications at the same time during a time range of daytime, and can execute, at the same time, a detection processing of a predetermined behavior (for example, a behavior that may trigger an incident such as dizziness of a person, quarrel, possession of a pistol, and shoplifting) performed by each of a plurality of persons that are captured and a detection processing of a person the same as or similar to each face image of a person registered (stored) in the registration database DB. 
     As described above, the learning model setting support system  100  according to the embodiment includes the terminal device P 1  that can receive a user operation, and at least one monitoring camera C 1  that can communicate with the terminal device, that includes artificial intelligence, and that captures an image of a monitoring area. The terminal device P 1  generates schedule information indicating information about a time range in which at least one learning model (application) used for artificial intelligence that detects a detection target (an example of an object) is validated based on a user operation, and transmits the schedule information to each of the monitoring cameras C 1 . Each of the monitoring cameras C 1  switches a learning model (an application) based on the transmitted schedule information. When a detection target is detected, each of the monitoring cameras C 1  generates and outputs an alarm indicating that the detection target is detected. 
     Accordingly, the learning model setting support system  100  according to the embodiment can efficiently support the setting of the schedule information indicating a time range of a learning model (an application) used in the artificial intelligence of each of the monitoring cameras C 1 . The learning model setting support system  100  can switch a learning model (an application) used in the artificial intelligence of each of the monitoring cameras C 1  according to a time range, thereby improving usability during an operation of each monitoring camera. 
     Although various embodiments have been described above with reference to the drawings, it is needless to say that the present disclosure is not limited to such examples. It will be apparent to those skilled in the art that various alterations, modifications, substitutions, additions, deletions, and equivalents can be conceived within the scope of the claims, and it should be understood that such changes also belong to the technical scope of the present disclosure. Components in various embodiments described above may be combined freely within a range not deviating from the spirit of the invention. 
     INDUSTRIAL APPLICABILITY 
     The present disclosure is useful as a monitoring camera and a learning model setting support system that can efficiently support the setting of a monitoring camera by a user and improve usability during an operation of the monitoring camera.