Image processing system, image processing method, image quality control device and program

Abstract

A video processing system (30) comprises an image control device (10) and a detection device (20). The image control device (10) comprises: an image control unit (11) that controls images of regions of a video; and a transmission unit (12) that transmits the image-controlled video to the detection device (20). The detection device (20) comprises: a detection unit (21) that detects information related to a subject in the video transmitted by the transmission unit; and a reporting unit (22) that reports, to the image control device (10), the detection result of the detection unit (21). The image control device (10) also comprises a determination unit (13) that, in accordance with the detection result reported by the reporting unit (22), determines images of regions of the video controlled by the control unit (11).

Description

Image processing system, image processing method, and image quality control device

[0001] The present disclosure relates to an image processing system, an image processing method, and an image quality control device.

[0002] There is a technology that analyzes video captured on-site at a remote location to recognize actions and objects. In order to reduce communication load, a device installed on-site determines the area of ​​interest, reduces the image quality of areas other than that area, and transmits the video to the means that will perform the analysis.

[0003] A related technique is known, for example, from Patent Document 1. Patent Document 1 describes a technique for transmitting video via a network in a device that transmits video so as to improve the image quality of an area that a viewer is gazing at.

[0004] Japanese Patent Application Laid-Open No. 2020-43533

[0005] Related technologies such as those disclosed in Patent Document 1 can reduce the amount of video data to be transmitted to a certain extent by suppressing the image quality of areas other than the gaze area. However, these related technologies always maintain high image quality in the gaze area, so there are cases where the data amount cannot be reduced appropriately. For example, when there are many gaze areas, it is difficult to reduce the data amount because there are few areas where the image quality can be reduced. Furthermore, while lowering the image quality of the entire video reduces the data amount, there is a risk of a decrease in the recognition rate at the receiving end.

[0006] In view of the above problems, the present disclosure aims to provide a video processing system, a video processing method, and an image quality control device that are capable of appropriately controlling the amount of video data.

[0007] The image processing system of the present disclosure comprises an image quality control device and a detection device, wherein the image quality control device comprises an image quality control means for controlling the image quality of each region of the image and a transmission means for transmitting the image quality-controlled image to the detection device, wherein the detection device comprises a detection means for detecting information regarding an object in the image transmitted from the transmission means and a notification means for notifying the image quality control device of the detection result of the detection means, and the image quality control device further comprises a determination means for determining the image quality of each region of the image controlled by the image quality control means in accordance with the detection result notified by the notification means.

[0008] The image processing method of the present disclosure is an image processing method in an image processing system including an image quality control device and a detection device, wherein the image quality control device controls the image quality of each area of ​​the image and transmits the image with the controlled image quality to the detection device, the detection device detects information regarding an object in the transmitted image and notifies the image quality control device of the detected detection result, and the image quality control device determines the image quality of each area of ​​the image to be controlled based on the notified detection result.

[0009] The image quality control device of the present disclosure comprises an image quality control means for controlling the image quality of each area of ​​an image, a transmission means for transmitting the image with controlled image quality to a detection device for detecting information regarding objects in the image, and a determination means for determining the image quality of each area of ​​the image controlled by the image quality control means in accordance with the detection results notified by the detection device.

[0010] According to the present disclosure, it is possible to provide a video processing system, a video processing method, and an image quality control device that are capable of appropriately controlling the amount of video data.

[0011] 1 is a configuration diagram showing an overview of a video processing system according to an embodiment. FIG. 1 is a configuration diagram showing an overview of an image quality control device according to an embodiment. FIG. 2 is a configuration diagram showing an overview of a detection device according to an embodiment. FIG. 3 is a configuration diagram showing an overview of a video processing system according to an embodiment. FIG. 4 is a diagram for explaining an overview of a video processing method according to an embodiment. FIG. 5 is a configuration diagram showing a basic configuration of a remote monitoring system according to an embodiment. FIG. 6 is a configuration diagram showing an example of a configuration of a terminal according to embodiment 1. FIG. 7 is a configuration diagram showing an example of a configuration of a center server according to embodiment 1. FIG. 8 is a flowchart showing an example of operation of the remote monitoring system according to embodiment 1. FIG. 9 is a flowchart showing an example of operation of sharpening region switching processing according to embodiment 1. FIG. 10 is a diagram for explaining video acquisition processing according to embodiment 1. FIG. 11 is a diagram for explaining object detection processing according to embodiment 1. FIG. 12 is a diagram for explaining sharpening region determination processing according to embodiment 1. FIG. 13 is a diagram for explaining sharpening region switching processing according to embodiment 1. FIG. 14 is a configuration diagram showing an example of a configuration of a terminal according to embodiment 2. FIG. 15 is a configuration diagram showing an example of a configuration of a center server according to embodiment 2. FIG. 16 is a configuration diagram showing an example of a configuration of a terminal according to embodiment 3. FIG. 17 is a configuration diagram showing an example of a configuration of a center server according to embodiment 3. FIG. 18 is a configuration diagram showing an overview of hardware of a computer according to an embodiment.

[0012] Hereinafter, embodiments will be described with reference to the drawings. In the drawings, the same elements are denoted by the same reference numerals, and redundant description will be omitted as necessary.

[0013] (Outline of the embodiment) First, an outline of the embodiment will be described. Fig. 1 shows a schematic configuration of a video processing system 30 according to the embodiment. The video processing system 30 can be applied to, for example, a remote monitoring system that transmits on-site video via a network and monitors the transmitted video.

[0014] As shown in Fig. 1, the image processing system 30 includes an image quality control device 10 and a detection device 20. The image quality control device 10 is a device that controls the image quality of images captured on-site. The detection device 20 is a device that detects objects, etc. from images whose image quality has been controlled by the image quality control device 10. For example, the image quality control device 10 may be a terminal, and the detection device 20 may be a server. The image quality control device 10 or the detection device 20 may be implemented on a cloud using virtualization technology, etc.

[0015] Fig. 2 shows a schematic configuration of the image quality control device 10, and Fig. 3 shows a schematic configuration of the detection device 20. As shown in Fig. 2, the image quality control device 10 includes an image quality control unit 11, a transmission unit 12, and a determination unit 13.

[0016] The image quality control unit 11 controls the image quality of each region of the image. For example, the image includes an object such as a person performing work or a work object used by the person during work, and the image quality control unit 11 controls the image quality of the region including the object. For example, the image quality control unit 11 may sharpen the region including the object, or may sharpen the region including an object selected according to predetermined conditions. In other words, the image quality of the region including the object may be higher than that of other regions, and the image quality of other regions may be lower. The transmission unit 12 transmits the image quality-controlled image to the detection device 20 via a network.

[0017] As shown in FIG. 3 , the detection device 20 includes a detection unit 21 and a notification unit 22. The detection unit 21 receives the video transmitted from the transmission unit 12 and detects information about an object in the received video. For example, the detection unit 21 may detect an object in the video as information about the object, or may recognize the behavior of the object detected in the video. The notification unit 22 notifies the image quality control device 10 of the detection result of the detection unit 21 via a network. For example, when the detection unit 21 detects an object, the notification unit 22 notifies the type of the detected object, and when the detection unit 21 recognizes the behavior of the object, the notification unit 22 notifies the type of behavior of the recognized object.

[0018] The determination unit 13 of the image quality control device 10 determines the image quality of each region of the image controlled by the image quality control unit 11 in accordance with the detection result notified by the notification unit 22. The determination unit 13 determines the image quality of each region of the image in accordance with whether information about an object has been detected by the detection unit 21. For example, when the detection unit 21 detects an object, the determination unit 13 determines the image quality of each region of the image in accordance with the object detection result. When the detection unit 21 recognizes the object's behavior, the determination unit 13 determines the image quality of each region of the image in accordance with the object's behavior recognition result. When information about an object has been detected, the determination unit 13 may change the image quality of the detected region from the image quality of other regions. For example, when behavior or an object is detected in a sharpened region, the determination unit 13 determines that further analysis of the detected region is unnecessary, excludes the detected region from the sharpened region, and determines the other regions as the sharpened region. In other words, the determination unit 13 may determine the detected region as a low-image-quality region and determine the other regions as high-image-quality regions. Furthermore, when information about an object has not been detected, the determination unit 13 may maintain the image quality of each region of the image. For example, if no action or object is detected in the sharpened region, it is determined that further analysis is necessary, and sharpening of the region continues.

[0019] The video processing system 30 may be configured with one device or multiple devices. As shown in Fig. 4, the video processing system 30 is not limited to the device configuration shown in Figs. 2 and 3, but may include an image quality control unit 11, a transmission unit 12, a determination unit 13, a detection unit 21, and a notification unit 22. Part or all of the video processing system 30 may be located on an edge or in the cloud. For example, in a system that monitors video captured at a site via a network, an edge is a device located at or near the site, and is also a device that is close to the terminal in terms of the network hierarchy.

[0020] 5 shows a video processing method according to an embodiment. For example, the video processing method according to the embodiment is executed by the image quality control device 10 and the detection device 20 of the video processing system 30 shown in FIGS. 1 to 3.

[0021] As shown in Fig. 5, first, the image quality control device 10 controls the image quality of each region of the image (S11). The image quality control device 10 detects an object from the camera image and controls the image quality based on the object detection result. For example, the image quality control device 10 sharpens the region including the object. Next, the image quality control device 10 transmits the image with the controlled image quality to the detection device 20 via a network (S12).

[0022] Next, the detection device 20 receives the transmitted video and detects information about the object in the received video (S13). For example, the detection device 20 recognizes the behavior of the object in the video. Next, the detection device 20 notifies the image quality control device 10 of the detected detection result via the network (S14). For example, the detection device 20 notifies the image quality control device 10 of the object's behavior recognition result.

[0023] Next, the image quality control device 10 determines the image quality of each area of ​​the video to be controlled in accordance with the notified detection result (S15). For example, the image quality control device 10 determines the area to be sharpened in accordance with the behavior recognition result of the detection device 20. For example, areas in which behavior has already been recognized are excluded from the sharpening area, and other areas are determined to be the sharpening area. When there are multiple sharpening areas, the sharpening areas may be narrowed down based on the behavior recognition result. Furthermore, returning to S11, the image quality control device 10 controls the image quality of each area of ​​the video in accordance with the determined image quality.

[0024] In a system that transmits video from a terminal, such as an image quality control device, to a server, such as a detection device, when transmitting video from the terminal to the server, if there are many areas that require high image quality, it may be difficult to achieve high image quality for all of the video. In this case, even if an attempt is made to lower the bit rate to address network conditions or reduce communication load, the bit rate cannot be reduced. For example, the bit rate cannot be reduced when a large number of people are captured in the video, or when the construction machinery or tools to be recognized occupy a large portion of the screen. On the other hand, the server cannot lower the image quality of the entire video because the recognition accuracy of the areas with low image quality decreases. Therefore, in an embodiment, the server notifies the terminal of the recognition results of objects and actions, and the terminal controls the image quality of each area of ​​the video according to the recognition results. This makes it possible to ensure the required recognition accuracy while suppressing the bit rate (communication volume).

[0025] (Basic Configuration of Remote Monitoring System) Next, a remote monitoring system, which is an example of a system to which the embodiment is applied, will be described. FIG. 6 illustrates the basic configuration of a remote monitoring system 1. The remote monitoring system 1 is a system that monitors an area captured by a camera using video. In this embodiment, the system will be described as a system that remotely monitors the work of workers at a site. For example, the site may be a work site such as a construction site or factory, a public square where people gather, a station, or a school, or any other area where people and machines are operating. In this embodiment, the work will be described as construction work, civil engineering work, etc., but is not limited to this. Note that video includes multiple images, i.e., frames, in a time series, so the terms video and image are interchangeable. In other words, the remote monitoring system can be described as a video processing system that processes video and an image processing system that processes images.

[0026] 6, the remote monitoring system 1 includes a plurality of terminals 100, a center server 200, a base station 300, and an MEC 400. The terminals 100, the base station 300, and the MEC 400 are located on the site side, and the center server 200 is located on the center side. For example, the center server 200 is located in a data center or the like that is located away from the site. The site side is also referred to as the edge side of the system, and the center side is also referred to as the cloud side.

[0027] The terminal 100 and the base station 300 are communicatively connected via a network NW1. The network NW1 is, for example, a wireless network such as 4G, local 5G / 5G, LTE (Long Term Evolution), or wireless LAN. The network NW1 is not limited to a wireless network and may be a wired network. The base station 300 and the center server 200 are communicatively connected via a network NW2. The network NW2 includes, for example, a core network such as 5GC (5th Generation Core network) or EPC (Evolved Packet Core), or the Internet. The network NW2 is not limited to a wired network and may be a wireless network. It can also be said that the terminal 100 and the center server 200 are communicatively connected via the base station 300. The base station 300 and the MEC 400 are communicatively connected via any communication method, but the base station 300 and the MEC 400 may be a single device.

[0028] The terminal 100 is a terminal device connected to the network NW1 and also serves as a video transmission device that transmits video of the site. The terminal 100 also serves as an image quality control device that controls the image quality of the video of the site. The terminal 100 acquires video captured by a camera 101 installed at the site and transmits the acquired video to the center server 200 via the base station 300. The camera 101 may be located outside the terminal 100 or inside the terminal 100.

[0029] The terminal 100 compresses video from the camera 101 to a predetermined bit rate and transmits the compressed video. The terminal 100 has a compression efficiency optimization function 102 that optimizes compression efficiency and a video transmission function 103. The compression efficiency optimization function 102 performs ROI control that controls the image quality of a ROI (Region of Interest) in the video. The ROI is a predetermined region within the video. The ROI may be a region including a recognition target of the video recognition function 201 of the center server 200, or may be a region where a user should focus their attention. The compression efficiency optimization function 102 reduces the bit rate by maintaining the image quality of the ROI including a person or object while lowering the image quality of the surrounding region. The video transmission function 103 transmits the video with controlled image quality to the center server 200. The compression efficiency optimization function 102 may include an image quality control unit that controls the image quality of each region of the video. The terminal 100 may also include a transmission unit that transmits the image quality-controlled video and a determination unit that determines the image quality of each region of the video controlled by the image quality control unit.

[0030] The base station 300 is a base station device of the network NW1, and also a relay device that relays communication between the terminal 100 and the center server 200. For example, the base station 300 is a local 5G base station, a 5G gNB (next generation Node B), an LTE eNB (evolved Node B), a wireless LAN access point, or the like, but may also be another relay device.

[0031] The MEC (Multi-access Edge Computing) 400 is an edge processing device located on the edge side of the system. The MEC 400 is an edge server that controls the terminal 100 and has a compression bit rate control function 401 and a terminal control function 402 that control the terminal bit rate. The compression bit rate control function 401 controls the bit rate of the terminal 100 through adaptive video distribution control and QoE (quality of experience) control. Adaptive video distribution control is a video distribution control method that controls the bit rate of video to be distributed according to network conditions. For example, the compression bit rate control function 401 predicts the recognition accuracy obtained when inputting the video to a recognition model by suppressing the bit rate of the distributed video according to the communication environment of the networks NW1 and NW2, and allocates a bit rate to the video distributed by the camera 101 of each terminal 100 to improve the recognition accuracy. The terminal control function 402 controls the terminal 100 to transmit video at the allocated bit rate. The terminal 100 encodes the video to the allocated bit rate and transmits the encoded video. It should be noted that the control is not limited to the bit rate control, but the frame rate of the video to be distributed may also be controlled depending on the network conditions.

[0032] The center server 200 is a server installed on the center side of the system. The center server 200 may be one or more physical servers, or may be a cloud server built on the cloud or other virtualized server. The center server 200 is a monitoring device that monitors on-site work by analyzing and recognizing on-site camera images. The center server 200 is also a video receiving device that receives images transmitted from the terminal 100. The center server 200 is also a detection device that detects objects, etc. from images whose image quality has been controlled by the terminal 100.

[0033] The center server 200 has a video recognition function 201, an alert generation function 202, a GUI drawing function 203, and a screen display function 204. The video recognition function 201 inputs the video transmitted from the terminal 100 into a video recognition AI (Artificial Intelligence) engine to recognize the work performed by the worker, i.e., the type of human behavior. The video recognition function 201 may include a detection unit that detects information about objects in the video. The center server 200 may also include a notification unit that notifies the terminal 100 of the detection result of the detection unit.

[0034] The alert generation function 202 generates an alert in response to the recognized work. The GUI drawing function 203 displays a GUI (Graphical User Interface) on the screen of the display device. The screen display function 204 displays an image of the terminal 100, the recognition results, an alert, etc. on the GUI. Note that any of the functions may be omitted or provided as necessary. For example, the center server 200 may not be provided with the alert generation function 202, the GUI drawing function 203, or the screen display function 204.

[0035] (First Embodiment) Next, a description will be given of a first embodiment. In this embodiment, an example in which a sharpened region is determined based on a behavior recognition result will be described.

[0036] First, the configuration of the remote monitoring system according to this embodiment will be described. The basic configuration of the remote monitoring system 1 according to this embodiment is as shown in Fig. 6. Here, configuration examples of the terminal 100 and the center server 200 will be described. Fig. 7 shows a configuration example of the terminal 100 according to this embodiment, and Fig. 8 shows a configuration example of the center server 200 according to this embodiment.

[0037] The configuration of each device is an example, and other configurations may be used as long as the operation according to the present embodiment described below is possible. For example, some of the functions of the terminal 100 may be arranged in the center server 200 or another device, or some of the functions of the center server 200 may be arranged in the terminal 100 or another device. Furthermore, the functions of the MEC 400, including the compression bit rate control function, may be arranged in the center server 200, the terminal 100, etc. Furthermore, the center server 200 may be implemented on a cloud.

[0038] 7 , the terminal 100 includes an image acquisition unit 110, an object detection unit 120, a sharpening region determination unit 130, an image quality control unit 140, a terminal communication unit 150, and a behavior recognition result acquisition unit 160. For example, the terminal 100 corresponds to the image quality control device 10 in FIG. 1 .

[0039] The video acquisition unit 110 acquires video captured by the camera 101. The video captured by the camera is also referred to as input video hereinafter. For example, the input video may include images of workers working on-site and objects used by the workers. The video acquisition unit 110 is also an image acquisition unit that acquires multiple images, i.e., frames, in time series.

[0040] The object detection unit 120 detects objects in the acquired input video. The object detection unit 120 detects objects in each image included in the input video and recognizes the type of the detected object. The object type may be represented by an object label or object class. For example, the object detection unit 120 may identify the type of object in the video and assign a label or class corresponding to the identified type. The object detection unit 120 extracts a rectangular area containing an object from each image included in the input video and recognizes the object type of the object in the extracted rectangular area. The rectangular area is a bounding box or an object area. Note that the object area containing the object is not limited to a rectangular area and may be a circular area, an irregular silhouette area, or the like. The object detection unit 120 calculates image features of the object included in the rectangular area and recognizes the object based on the calculated features. For example, the object detection unit 120 recognizes objects in the image using an object recognition engine that uses machine learning such as deep learning. Objects can be recognized by machine learning the image features and object type of the object. The object detection result includes the object type, position information of the rectangular area containing the object, and the object type score. The object position information is, for example, the coordinates of each vertex of the rectangular area, but it may also be the position of the center of the rectangular area or the position of any point on the object. The object type score is the likelihood of the detected object type, i.e., the reliability or certainty.

[0041] The behavior recognition result acquisition unit 160 acquires the behavior recognition result received by the device communication unit 150 from the center server 200. The behavior recognition result includes the behavior type, the behavior type score, the type of object of the recognized behavior, and the position information of a rectangular area including the object. The behavior type may be represented by a behavior label or a behavior class. For example, a label or class corresponding to the type of behavior recognized from the video may be assigned. The behavior type score is the likelihood of the recognized behavior type, i.e., the reliability or certainty. The object indicated by the behavior recognition result is, for example, a person who is the target of behavior recognition, but may also include a work object used by the person in work. The behavior recognition result may also include an image of the object area, feature values, importance, etc. The importance is the importance of the recognized behavior and may be a priority for clarifying it.

[0042] The sharpening region determination unit 130 determines a sharpening region for sharpening the image quality of the acquired input video based on the detection result of an object detected in the input video. The sharpening region determination unit 130 may determine the regions of all detected objects as the sharpening region. Alternatively, the sharpening region determination unit 130 may determine the sharpening region based on position information of objects having a predetermined object type among the detected objects detected in the input video. For example, the sharpening region may be selected as the sharpening region from the region of an object having an object type in a gazing target list stored in the storage unit of the terminal 100. Alternatively, the sharpening region may be selected as the sharpening region from the region of an object having an object type score greater than a predetermined value or the region of a predetermined number of objects in descending order of the object type score.

[0043] Furthermore, when the sharpening region determination unit 130 acquires a behavior recognition result from the center server 200, the sharpening region determination unit 130 corresponds to the determination unit 13 in FIG. 1 . The sharpening region determination unit 130 determines a sharpening region in the input video based on the acquired behavior recognition result. For example, the sharpening region determination unit 130 may determine a sharpening region based only on the object detection result or only on the behavior recognition result, or may determine a sharpening region based on the object detection result and the behavior recognition result. For example, the sharpening region determination unit 130 may determine a sharpening region by narrowing down a region selected based on the object detection result based on the behavior recognition result. When the behavior recognition result is not acquired from the center server 200, for example, before the center server 200 performs behavior recognition, the sharpening region determination unit 130 may determine a sharpening region based only on the object detection result. As described below, upon acquiring a behavior recognition result, the sharpening region determination unit 130 switches the sharpening region in the input video based on the acquired behavior recognition result. The sharpening region determination unit 130 determines whether to sharpen a region indicated by object position information included in the behavior recognition result, depending on whether the object's behavior is recognized. When multiple objects are detected in the input video, the sharpening region determination unit 130 may match the region in which the object is detected with the region indicated by the behavior recognition result, and determine whether to sharpen the object detection region narrowed down by the matching result. For example, when the object's behavior is recognized, the sharpening region determination unit 130 excludes the region indicated by the recognition result from the sharpening region, and selects another region as the sharpening region. When the object's behavior is not recognized, the sharpening region determination unit 130 selects the region indicated by the recognition result as the sharpening region. In other words, the sharpening region determination unit 130 continues to sharpen the region indicated by the recognition result. For example, whether the object's behavior is recognized may be determined based on the score of the behavior type in the behavior recognition result. Furthermore, when the behavior recognition result includes importance, the sharpening region determination unit 130 may determine the sharpening region based on the importance. For example, the sharpening region determination unit 130 may assign a priority to each region according to the behavior type or importance, and determine the sharpening region based on the assigned priority. In this case, the area with the highest priority may be determined as the sharpening area, or a predetermined number of areas in descending order of priority may be determined as the sharpening areas. Furthermore, the time for sharpening the area indicated by the behavior recognition result may be determined according to the behavior recognition result.For example, a time for clarifying may be associated with each behavior in advance, and the time for clarifying or the time for excluding from the clarifying may be determined according to the behavior type of the behavior recognition result. Note that the center server 200 may determine the sharpening area according to the behavior recognition result, and the center server may notify the terminal 100 of information about the sharpening area.

[0044] The image quality control unit 140 controls the image quality of the input video based on the determined sharpening region. For example, the image quality control unit 140 corresponds to the image quality control unit 11 in FIG. 1 . The sharpening region is a region in which image quality is sharpened more than other regions, i.e., a high-image-quality region in which image quality is increased more than other regions. The sharpening region is also a ROI. The other regions are low-image-quality regions or non-sharp regions. The image quality control unit 140 is an encoder that encodes the input video using a predetermined encoding method. For example, the image quality control unit 140 encodes using a video encoding method such as H.264 or H.265. The image quality control unit 140 compresses the sharpening region and other regions at predetermined compression rates, i.e., bit rates, respectively, to encode the image quality of the sharpening region to a predetermined quality. In other words, by changing the compression rates of the sharpening region and other regions, the sharpening region is made higher in image quality than other regions. It can also be said that the other regions are made lower in image quality than the sharpening region. For example, the image quality can be decreased by gradual change in pixel values ​​between adjacent pixels.

[0045] The image quality control unit 140 may also encode the input video so that it conforms to the bit rate allocated by the compression bit rate control function 401 of the MEC 400. The image quality of the high-image-quality region and the low-image-quality region may be controlled within the allocated bit rate range. The image quality control unit 140 may also determine the bit rate based on the communication quality between the terminal 100 and the center server 200. The image quality of the high-image-quality region and the low-image-quality region may be controlled within a bit rate range based on the communication quality. The communication quality may be, for example, a communication speed, but may also be other indicators such as a transmission delay or an error rate. The terminal 100 may also include a communication quality measurement unit that measures the communication quality. For example, the communication quality measurement unit determines the bit rate of the video to be transmitted from the terminal 100 to the center server 200 according to the communication speed. The base station 300 or the center server 200 may measure the communication speed based on the amount of data received, and the communication quality measurement unit may acquire the measured communication speed from the base station 300 or the center server 200. The communication quality measurement unit may also estimate the communication speed based on the amount of data transmitted from the terminal communication unit 150 per unit time.

[0046] The device communication unit 150 transmits the encoded data encoded by the image quality control unit 140 to the center server 200 via the base station 300. The device communication unit 150 is a transmission unit that transmits the image quality-controlled video. For example, the device communication unit 150 corresponds to the transmission unit 12 in FIG. 1 . The device communication unit 150 also serves as a reception unit that receives the behavior recognition result transmitted from the center server 200 via the base station 300. The device communication unit 150 is an interface capable of communicating with the base station 300, and is, for example, a wireless interface such as 4G, local 5G / 5G, LTE, or wireless LAN, but may also be a wireless or wired interface of any other communication method. The device communication unit 150 may include a first device communication unit that transmits the encoded data and a second device communication unit that receives the behavior recognition result. The first device communication unit and the second device communication unit may be communication units of the same communication method or communication units of different communication methods.

[0047] 8, the center server 200 includes a center communication unit 210, a decoder 220, an object detection unit 230, an object tracking unit 240, a feature extraction unit 250, a posture estimation unit 260, a behavior recognition unit 270, and a behavior recognition result notification unit 280. For example, the center server 200 corresponds to the detection device 20 in FIG. 2.

[0048] The center communication unit 210 receives encoded data transmitted from the terminal 100 via the base station 300. The center communication unit 210 is a receiving unit that receives video with controlled image quality. The center communication unit 210 also serves as a transmitting unit that transmits behavior recognition results recognized by the behavior recognition unit 270 to the terminal 100 via the base station 300. The center communication unit 210 is an interface capable of communicating with the Internet or a core network, and is, for example, a wired interface for IP communication, but may also be a wired or wireless interface of any other communication method. The center communication unit 210 may include a first center communication unit that receives encoded data and a second center communication unit that transmits behavior recognition results. The first center communication unit and the second center communication unit may be communication units of the same communication method or communication units of different communication methods.

[0049] The decoder 220 decodes encoded data received from the terminal 100. The decoder 220 is a decoding unit that decodes encoded data. The decoder 220 also functions as a restoration unit that restores encoded data, i.e., compressed data, using a predetermined encoding method. The decoder 220 decodes using a video encoding method such as H.264 or H.265, which is compatible with the encoding method of the terminal 100. The decoder 220 performs decoding according to the compression ratio and bit rate of each region, and generates decoded video. The decoded video is also referred to as received video hereinafter.

[0050] The object detection unit 230 detects objects in the received video received from the terminal 100. For example, like the object detection unit 120 of the terminal 100, the object detection unit 230 recognizes objects using an object recognition engine that uses machine learning. That is, the object detection unit 230 extracts a rectangular area including an object from each image of the received video, and recognizes the object type of the object in the extracted rectangular area. The object detection result includes the object type, position information of the rectangular area including the object, a score for the object type, etc.

[0051] The object tracking unit 240 tracks, i.e., tracks, detected objects in the received video. Based on the object detection results, the object tracking unit 240 matches objects in each image included in the received video and associates the matched objects in each image. For example, each object may be identified and tracked by assigning a tracking ID to the detected object. For example, the object is tracked by associating objects between images based on the distance or overlap between the rectangular area of ​​the object detected in the previous image and the rectangular area of ​​the object detected in the next image.

[0052] The feature extraction unit 250 extracts image features of each object tracked by the object tracking unit 240. The feature extraction unit 250 extracts features that are used by the behavior recognition unit 270 to recognize the behavior of the object. The feature extraction unit 250 may extract two-dimensional spatial features of the image or spatiotemporal features in the time direction. For example, the feature extraction unit 250 extracts image features of the object using a feature extraction engine that uses machine learning such as deep learning. The feature extraction engine may be a convolutional neural network (CNN) or a recurrent neural network (RNN), or other neural networks.

[0053] The posture estimation unit 260 estimates the posture of each object tracked by the object tracking unit 240. The posture estimation unit 260 may estimate the skeleton of a person, which is the detected object, as the posture of the object. For example, the posture estimation unit 260 estimates the posture of the object in the image using a skeleton estimation engine or posture estimation engine that uses machine learning such as deep learning.

[0054] The behavior recognition unit 270 recognizes the behavior of an object based on the feature extraction results and the posture estimation results. For example, the behavior recognition unit 270 corresponds to the detection unit 21 in FIG. 2 . Note that the object detection unit 230 may also correspond to the detection unit 21 in FIG. 2 . The behavior recognition unit 270 recognizes the behavior of an object based on the extracted image feature of the object and the estimated posture of the object. For example, the behavior recognition unit 270 recognizes work performed by a person using an object, unsafe behavior that puts a person in a dangerous state, etc. Note that the method is not limited to behavior recognition, and other video recognition processing may also be used. The behavior recognition unit 270 recognizes the type of object behavior for each object. For example, the behavior recognition unit 270 recognizes the behavior of an object using a behavior recognition engine that uses machine learning such as deep learning. The behavior of a person in a video can be recognized by machine learning the features and behavior types of a video of a person performing work. The behavior recognition engine may be a CNN, an RNN, or another neural network. As described above, the behavior recognition result includes the behavior type, the behavior type score, the object type, and object location information. The object type and location information are the type and location information of the object detected by the object detection unit 230. The behavior recognition result may include an image and feature amounts of the area of ​​the detected object. Furthermore, the behavior type and object type may be associated with a level of importance, and the behavior recognition result may include a level of importance according to the recognized behavior type or object type.

[0055] The behavior recognition result notification unit 280 notifies the terminal 100 of the behavior recognition result, which is a result of recognizing the behavior of the object. For example, the behavior recognition result notification unit 280 corresponds to the notification unit 22 in Fig. 2. The behavior recognition result notification unit 280 transmits the behavior recognition result output by the behavior recognition unit 270 to the terminal 100 via the center communication unit 210.

[0056] Next, the operation of the remote monitoring system according to this embodiment will be described. Fig. 9 shows an example of the operation of the remote monitoring system 1 according to this embodiment, and Fig. 10 shows an example of the operation of the sharpening area switching process (S124) in Fig. 9. For example, the following description will be given assuming that the terminal 100 executes S111 to S115 and S123 to S124, and the center server 200 executes S116 to S122, but this is not limiting and any device may execute each process.

[0057] As shown in Fig. 9, the terminal 100 acquires video from the camera 101 (S111). The camera 101 generates video of the work site, and the video acquisition unit 110 acquires the video output from the camera 101, i.e., the input video. For example, as shown in Fig. 11, the image of the input video includes three people P1 to P3 working on the work site. For example, the person P3 is working with a hammer.

[0058] Next, the terminal 100 detects objects based on the acquired input video (S112). The object detection unit 120 uses an object recognition engine to detect rectangular areas within an image included in the input video and recognize the object type of the object within the detected rectangular area. For each detected object, the object detection unit 120 outputs the object type, position information of the object's rectangular area, a score for the object type, and the like as object detection results. For example, when object detection is performed from the image of FIG. 11, people P1 to P3 and a hammer are detected, and the rectangular areas of people P1 to P3 and the hammer are detected, as shown in FIG. 12.

[0059] Next, the terminal 100 determines a sharpening region based on the object detection result (S113). At this stage, the center server 200 has not yet recognized any behavior from the video, so the sharpening region is determined without using the behavior recognition result. For example, the sharpening region determination unit 130 may determine the regions of all objects or the regions of objects having a predetermined object type as the sharpening region. Alternatively, the sharpening region determination unit 130 may determine the region of an object whose object type score is greater than a predetermined value as the sharpening region. The region of the object selected as the sharpening region is set as the currently selected sharpening region. For example, in the example of FIG. 12, if the score of person P1 is greater than a predetermined value and the scores of person P2, person P3, and the hammer are less than the predetermined value, the rectangular region of person P1 is determined as the sharpening region, as shown in FIG. 13.

[0060] Next, the terminal 100 encodes the input video based on the determined sharpening region (S114). The image quality control unit 140 encodes the input video using a predetermined video encoding method. For example, the image quality control unit 140 may encode the input video at a bit rate assigned by the compression bit rate control function 401 of the MEC 400, or at a bit rate corresponding to the communication quality between the terminal 100 and the center server 200. The image quality control unit 140 encodes the input video so that the sharpening region has higher image quality than other regions within the assigned bit rate or the bit rate corresponding to the communication quality. For example, the compression rate of the sharpening region is lowered compared to the compression rates of other regions, thereby increasing the image quality of the sharpening region and decreasing the image quality of the other regions. As shown in FIG. 13 , when the rectangular region of person P1 is selected as the sharpening region, the rectangular region of person P1 is increased in image quality, and the other regions including person P2, person P3, and the hammer are decreased in image quality.

[0061] Next, the terminal 100 transmits the encoded data to the center server 200 (S115), and the center server 200 receives the encoded data (S116). The terminal communication unit 150 transmits the encoded data obtained by encoding the input video to the base station 300. The base station 300 transfers the received encoded data to the center server 200 via the core network or the Internet. The center communication unit 210 receives the transferred encoded data from the base station 300.

[0062] Next, the center server 200 decodes the received encoded data (S117). The decoder 220 decodes the encoded data in accordance with the compression ratio and bit rate of each region, and generates decoded video, that is, received video.

[0063] Next, the center server 200 detects an object in the received video based on the received video (S118). The object detection unit 230 detects an object in the received video using an object recognition engine. The object detection unit 230 outputs the type of the detected object, position information of the rectangular area including the object, a score for the object type, etc. as the object detection result.

[0064] Next, the center server 200 tracks the detected objects in the received video (S119). The object tracking unit 240 tracks the objects in the received video based on the object detection results in the received video. The object tracking unit 240 assigns a tracking ID to each detected object and tracks the object identified by the tracking ID in each image.

[0065] Next, the center server 200 extracts feature amounts from the image of each tracked object and estimates the posture of the object (S120). The feature extraction unit 250 uses a feature extraction engine to extract feature amounts from the image of the tracked object. The posture estimation unit 260 uses the posture estimation engine to estimate the posture of the tracked object.

[0066] Next, the center server 200 recognizes the behavior of the object based on the feature extraction results and the posture estimation results (S121). The behavior recognition unit 270 uses a behavior recognition engine to recognize the behavior of the object in the received video based on the extracted feature amounts of the object and the estimated posture of the object. The behavior recognition unit 270 outputs the type of behavior of the recognized object, object position information, a behavior type score, etc. as the behavior recognition result. For example, as shown in FIG. 13 , when the rectangular area of ​​person P1 has been enhanced in image quality, person P1 is detected and tracked, and the behavior of person P1 is recognized based on the feature amounts and posture of person P1.

[0067] Next, the center server 200 notifies the terminal 100 of the recognized behavior recognition result (S122), and the terminal 100 acquires the behavior recognition result (S123). The behavior recognition result notifying unit 280 notifies the terminal of the behavior recognition result output by the behavior recognition unit 270 via the center communication unit 210. The center communication unit 210 transmits the behavior recognition result to the base station 300 via the Internet or a core network. The base station 300 transfers the received behavior recognition result to the terminal 100. The terminal communication unit 150 receives the transferred behavior recognition result from the base station 300. The behavior recognition result acquisition unit 160 acquires the behavior recognition result received by the terminal communication unit 150.

[0068] Next, the terminal 100 performs a sharpening area switching process to switch the sharpening area based on the acquired behavior recognition result (S124). In the sharpening area switching process, the sharpening area determination unit 130 selects a sharpening area based on the behavior recognition result and switches the sharpening area determined in S113. It may also determine whether to execute the sharpening area switching process. For example, if a predetermined time has passed since the previous execution of the sharpening area switching process, if a predetermined object or behavior has been recognized, or if the areas of all objects have already been sharpened, the sharpening area switching process may not be executed. In this case, the currently selected sharpening area may be reset, and a sharpening area may be determined based on the object detection result, as in S113.

[0069] In the sharpening area switching process, as shown in FIG. 10 , the sharpening area determination unit 130 matches the acquired behavior recognition results with the object detection results of the input video (S201). That is, the sharpening area determination unit 130 matches the objects whose behaviors are recognized by the center server 200 with the objects detected by the terminal 100, and extracts from the detected objects an object that matches the behavior-recognized object. The sharpening area determination unit 130 compares the objects in the behavior recognition results with the objects in the object detection results and determines whether the objects whose behaviors are recognized and the detected objects are the same, i.e., whether they match. The sharpening area determination unit 130 performs matching based on, for example, the object type, object position information, etc. For example, if the object types match and the distance between the objects is equal to or less than a predetermined threshold, it determines that they match. Furthermore, it may use the feature values ​​of the object images to determine that the object images are similar and therefore determine that they match. Note that if a matching object cannot be extracted, the sharpening area may be determined based on the object detection results, as in S113.

[0070] Next, the sharpening region determination unit 130 determines whether the behavior of the object matched with the behavior recognition result has been recognized (S202). If the score of the behavior type included in the behavior recognition result is greater than a predetermined value, the sharpening region determination unit 130 determines that the behavior has been recognized, and if the score of the behavior type is smaller than the predetermined value, the sharpening region determination unit 130 determines that the behavior has not been recognized.

[0071] If it is determined that the behavior has been recognized, the sharpening region determination unit 130 selects another region as the sharpening region (S203). If the behavior has been recognized, the sharpening region determination unit 130 excludes the region of the matched object, i.e., the region of the object currently selected as the sharpening region, from the sharpening region, and selects the region of another object as the sharpening region, thereby switching the sharpening region. The region of the object newly selected as the sharpening region is set as the currently selected sharpening region. If multiple object regions have been detected, the next region to be sharpened is selected from the regions not selected as the sharpening region, and the selected object region is sequentially switched each time a behavior is recognized. The next region to be sharpened may be selected based on the object type detected by object detection or the score of the object type, or may be selected randomly. Note that if there is no next region to be sharpened or if the behavior type is a predetermined behavior, the current selection of the sharpening region may be maintained without switching the sharpening region to another region. In other words, in this case, the region of the matched object may be selected as the sharpening region.

[0072] In the example of Figure 13, if the behavior of person P1 has been recognized, the area of ​​person P3 is excluded from the sharpening area, and one of person P2, person P3, and the hammer is selected as the sharpening area. For example, from the object detection result, the object type scores of person P2, person P3, and the hammer are compared, and if the object type score of person P2 is higher, the rectangular area of ​​person P2 is determined as the sharpening area, as shown in Figure 14. Thereafter, if the behavior of person P2 has been recognized, the rectangular area of ​​person P3 and the hammer is determined as the sharpening area, as shown in Figure 15.

[0073] If it is determined that the behavior has not been recognized, the sharpening region determination unit 130 selects the region of the matched object as the sharpening region (S204). That is, in this case, the current selection of the sharpening region is maintained. For example, in the example of FIG. 13, if the behavior of person P1 is not recognized, the rectangular region of person P1 remains selected as the sharpening region. Then, the processing from S114 onward is repeated.

[0074] As described above, in this embodiment, a sharpening area to be sharpened on the terminal is determined based on the behavior recognition result of the center server. For example, areas that have been recognized by the center server are temporarily excluded from the sharpening area, and other areas that have not been recognized are preferentially selected as the sharpening area. This makes it possible to narrow down important areas based on the object detection result of the terminal and the behavior recognition result of the center server, and to shift the sharpening area from recognized areas to unrecognized areas. By lowering the sharpening priority of areas that have already been recognized by the center server, a wider range of behaviors can be recognized, thereby reducing recognition failures. Therefore, it is possible to appropriately reduce the amount of video data transmitted from the terminal while maintaining the recognition accuracy of behavior recognition.

[0075] Next, a second embodiment will be described. In this embodiment, an example in which a sharpening region is determined based on an object detection result will be described. Note that this embodiment can be implemented in combination with the first embodiment, and the configurations shown in the first embodiment may be used as appropriate.

[0076] Fig. 16 shows an example of the configuration of the terminal 100 according to this embodiment, and Fig. 17 shows an example of the configuration of the center server 200 according to this embodiment. Here, the configuration that differs from the first embodiment will be mainly described.

[0077] As shown in Fig. 16 , the terminal 100 includes an object detection result acquisition unit 161 instead of the behavior recognition result acquisition unit 160 of the first embodiment. Also, as shown in Fig. 17 , the center server 200 includes an object detection result notification unit 281 instead of the behavior recognition result notification unit 280 of the first embodiment. The other configurations are the same as those of the first embodiment. Note that the terminal 100 may further include the object detection result acquisition unit 161 in addition to the configuration of the first embodiment. The center server 200 may further include the object detection result notification unit 281 in addition to the configuration of the first embodiment.

[0078] The object detection result notification unit 281 of the center server 200 notifies the terminal 100 of the object detection result detected by the center server 200. The object detection result notification unit 281 transmits the object detection result output by the object detection unit 230 to the terminal 100 via the center communication unit 210. The object detection result includes the object type, position information of the rectangular area including the object, a score for the object type, etc.

[0079] The object detection result acquisition unit 161 of the terminal 100 acquires the object detection result received from the center server 200 via the device communication unit 150. The sharpening region determination unit 130 determines a sharpening region in the input video based on the acquired object detection result. The method for determining a sharpening region based on the object detection result is the same as the method for determining a sharpening region based on the behavior recognition result in embodiment 1. That is, the sharpening region determination unit 130 determines whether to sharpen a region indicated by object position information included in the object detection result, depending on whether an object is detected. If an object is detected, for example, if the score for the object type is greater than a predetermined value, the sharpening region determination unit 130 excludes the region indicated by the detection result from the sharpening region and selects another region as the sharpening region. On the other hand, if an object is not detected, for example, if the score for the object type is less than a predetermined value, the sharpening region determination unit 130 selects the region indicated by the detection result as the sharpening region.

[0080] As described above, in this embodiment, the sharpening area to be sharpened at the terminal is determined based on the object detection result of the center server. Even in this case, as in the first embodiment, it is possible to appropriately reduce the amount of video data while ensuring the detection accuracy of object detection.

[0081] (Embodiment 3) Next, embodiment 3 will be described. In this embodiment, an example in which a sharpening area is determined based on a face authentication result will be described. Note that this embodiment can be implemented in combination with embodiment 1 or 2, and the configurations shown in embodiment 1 or 2 may be used as appropriate.

[0082] Fig. 18 shows an example of the configuration of a terminal 100 according to this embodiment, and Fig. 19 shows an example of the configuration of a center server 200 according to this embodiment. Here, the configuration that differs from embodiment 1 will be mainly described. Note that this embodiment may also be applied to embodiment 2.

[0083] As shown in Fig. 18 , the terminal 100 includes a face authentication result acquisition unit 162 instead of the behavior recognition result acquisition unit 160 of the first embodiment. Also, as shown in Fig. 19 , the center server 200 includes a face authentication unit 282 instead of the behavior recognition result notification unit 280 of the first embodiment. The other configurations are the same as those of the first embodiment. Note that the terminal 100 may further include the face authentication result acquisition unit 162 in addition to the configuration of the first embodiment. The center server 200 may further include the face authentication unit 282 in addition to the configuration of the first embodiment.

[0084] The face authentication unit 282 of the center server 200 performs face authentication of a person detected by object detection. For example, a memory unit stores an image of the person's face in association with identification information that identifies the person. The face authentication unit 282 extracts the person's face from the video and compares the extracted face with the faces of people registered in the memory unit. For example, the face authentication unit 282 may authenticate the face of the person in the image using a face authentication engine that uses machine learning such as deep learning. The face authentication unit 282 transmits the matching rate of the face authentication and the person's position information as a face authentication result to the terminal 100 via the center communication unit 210.

[0085] The face authentication result acquisition unit 162 of the terminal 100 acquires the face authentication result received from the center server 200 via the device communication unit 150. The sharpening area determination unit 130 determines a sharpening area in the input video based on the acquired face authentication result. The sharpening area determination unit 130 determines whether to sharpen an area indicated by the position information of a person included in the face authentication result, depending on whether the face has been authenticated. If the face has been authenticated, for example, if the matching rate is greater than a predetermined value, the sharpening area determination unit 130 excludes the area indicated by the face authentication result from the sharpening area and selects another area as the sharpening area. If the face has not been authenticated, for example, if the matching rate is less than a predetermined value, the sharpening area determination unit 130 selects the area indicated by the face authentication result as the sharpening area.

[0086] As described above, in this embodiment, the sharpening area to be sharpened at the terminal is determined based on the face authentication result of the center server. Even in this case, as in the first and second embodiments, the amount of video data can be appropriately reduced while ensuring the accuracy of behavior recognition and object detection.

[0087] The present disclosure is not limited to the above-described embodiment, and can be modified as appropriate within the scope of the present disclosure.

[0088] Each component in the above-described embodiments may be configured with hardware or software, or both, and may be configured with a single piece of hardware or software, or may be configured with multiple pieces of hardware or software. Each device and each function (processing) may be realized by a computer 40 having a processor 41 such as a CPU (Central Processing Unit) and a memory 42 serving as a storage device, as shown in FIG. 20 . For example, a program for performing the method (video processing method) in the embodiment may be stored in the memory 42, and each function may be realized by the processor 41 executing the program stored in the memory 42.

[0089] These programs include instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The programs may be stored on a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray disc or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The programs may also be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

[0090] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure.

[0091] Some or all of the above embodiments can be described as, but are not limited to, the following supplementary notes. (Supplementary Note 1) A video processing system comprising: an image quality control device; and a detection device, wherein the image quality control device comprises: image quality control means for controlling the image quality of each region of an image; and transmission means for transmitting the image quality-controlled image to the detection device, wherein the detection device comprises: detection means for detecting information about an object in the image transmitted from the transmission means; and notification means for notifying the image quality control device of the detection result of the detection means, and the image quality control device further comprises: determination means for determining the image quality of each region of the image controlled by the image quality control means in accordance with the detection result notified by the notification means. (Supplementary Note 2) The video processing system according to Supplementary Note 1, wherein the detection means detects an object in the image as information about the object, and the determination means determines the image quality of each region of the image controlled by the image quality control means in accordance with the detection result of the object. (Supplementary Note 3) The video processing system according to Supplementary Note 1 or 2, wherein the detection means recognizes the behavior of an object in the video as information about the object, and the determination means determines the image quality of each area of ​​the video controlled by the image quality control means according to the recognition result of the object's behavior. (Supplementary Note 4) The video processing system according to any one of Supplements 1 to 3, wherein the determination means determines the image quality of each area of ​​the video according to whether information about the object has been detected by the detection means. (Supplementary Note 5) The video processing system according to Supplementary Note 4, wherein, when information about the object has been detected by the detection means, the determination means changes the image quality of the area in which the object has been detected and the image quality of other areas. (Supplementary Note 6) The video processing system according to Supplementary Note 4 or 5, wherein, when information about the object has not been detected by the detection means, the determination means maintains the image quality of each area of ​​the video.(Supplementary Note 7) A video processing method in a video processing system including a picture quality control device and a detection device, wherein the picture quality control device controls the picture quality of each region of a video, and transmits the picture quality-controlled video to the detection device, the detection device detects information about an object in the transmitted video, and notifies the picture quality control device of the detected detection result, and the picture quality control device determines the picture quality of each region of the video to be controlled in accordance with the notified detection result. (Supplementary Note 8) The video processing method according to Supplementary Note 7, wherein the detection device detects an object in the video as information about the object, and the picture quality control device determines the picture quality of each region of the video to be controlled in accordance with the detection result of the object. (Supplementary Note 9) The video processing method according to Supplementary Note 7 or 8, wherein the detection device recognizes the behavior of the object in the video as information about the object, and the picture quality control device determines the picture quality of each region of the video to be controlled in accordance with the recognition result of the object's behavior. (Supplementary Note 10) The video processing method of any one of Supplementary Notes 7 to 9, wherein the image quality control device determines the image quality of each region of the video depending on whether information about the object has been detected. (Supplementary Note 11) The video processing method of Supplementary Note 10, wherein, when information about the object has been detected, the image quality control device changes the image quality of the region where the object has been detected and the image quality of other regions. (Supplementary Note 12) The video processing method of Supplementary Note 10 or 11, wherein, when information about the object has not been detected, the image quality control device maintains the image quality of each region of the video. (Supplementary Note 13) An image quality control device comprising: image quality control means for controlling the image quality of each region of the video; transmission means for transmitting the image with controlled image quality to a detection device that detects information about an object in the video; and determination means for determining the image quality of each region of the video controlled by the image quality control means depending on the detection result notified from the detection device. (Supplementary Note 14) The image quality control device according to Supplementary Note 13, wherein the detection device detects an object in the image as information relating to the object, and the determination means determines the image quality of each area of ​​the image controlled by the image quality control means according to the detection result of the object.(Supplementary Note 15) The image quality control device according to Supplementary Note 13 or 14, wherein the detection device recognizes the behavior of an object in the video as information about the object, and the determination means determines the image quality of each region of the video controlled by the image quality control means according to the recognition result of the behavior of the object. (Supplementary Note 16) The image quality control device according to any one of Supplementary Notes 13 to 15, wherein the determination means determines the image quality of each region of the video according to whether information about the object has been detected by the detection device. (Supplementary Note 17) The image quality control device according to Supplementary Note 16, wherein, when information about the object has been detected by the detection device, the determination means changes the image quality of the region in which the object has been detected and the image quality of other regions. (Supplementary Note 18) The image quality control device according to Supplementary Note 16 or 17, wherein, when information about the object has not been detected by the detection device, the determination means maintains the image quality of each region of the video.

[0092] 1 Remote monitoring system 10 Image quality control device 11 Image quality control unit 12 Transmission unit 13 Determination unit 20 Detection device 21 Detection unit 22 Notification unit 30 Image processing system 40 Computer 41 Processor 42 Memory 100 Terminal 101 Camera 102 Compression efficiency optimization function 103 Image transmission function 110 Image acquisition unit 120 Object detection unit 130 Sharpening area determination unit 140 Image quality control unit 150 Terminal communication unit 160 Behavior recognition result acquisition unit 161 Object detection result acquisition unit 162 Face authentication result acquisition unit 200 Center server 201 Image recognition function 202 Alert generation function 203 GUI drawing function 204 Screen display function 210 Center communication unit 220 Decoder 230 Object detection unit 240 Object tracking unit 250 Feature extraction unit 260 Posture estimation unit 270 Behavior recognition unit 280 Behavior recognition result notification unit 281 Object detection result notification unit 282 Face authentication unit 300 Base station 400 MEC 401 Compression bit rate control function 402 Terminal control function

Claims

1. An image quality control device and a detection device, The image quality control device includes: An image quality control means for controlling the image quality of each area of ​​the image; a transmitting means for transmitting the image quality controlled image to the detection device, The detection device includes: a detection means for detecting information regarding an object in the image transmitted from the transmission means; a notification means for notifying the image quality control device of a detection result of the detection means, The image quality control device includes: a determination unit that determines the image quality of each area of ​​the image controlled by the image quality control unit according to the detection result notified by the notification unit, Video processing system.

2. The detection means detects an object in the image as information related to the object, the determining means determines the image quality of each area of ​​the image controlled by the image quality control means in accordance with the result of the object detection. The video processing system according to claim 1 .

3. The detection means recognizes a behavior of an object in the video as information about the object, the determining means determines image quality of each area of ​​the image controlled by the image quality control means in accordance with a result of the recognition of the behavior of the object.

3. The video processing system according to claim 1.

4. the determining means determines image quality of each area of ​​the image depending on whether information about the object is detected by the detecting means.

3. The video processing system according to claim 1.

5. the determining means, when information about the object is detected by the detecting means, changes the image quality of the area in which the object is detected and the image quality of other areas. The video processing system according to claim 4.

6. the determining means maintains the image quality of each region of the image when the detection means has not detected information about the object. The video processing system according to claim 4.

7. An image processing method in an image processing system including an image quality control device and a detection device, The image quality control device includes: Controlling the image quality of each area of ​​the image, transmitting the quality controlled image to the detection device; The detection device includes: Detecting information about an object in the transmitted image; notifying the image quality control device of the detected result; The image quality control device includes: determining image quality of each area of ​​the image to be controlled according to the notified detection result; Image processing method.

8. The detection device detects an object in the image as information about the object; the image quality control device determines the image quality of each area of ​​the image to be controlled in response to the object detection result; The video processing method according to claim 7.

9. An image quality control means for controlling the image quality of each area of ​​the image; a transmitting means for transmitting the image quality controlled image to a detection device for detecting information relating to objects in the image; a determination means for determining the image quality of each area of ​​the image controlled by the image quality control means in accordance with the detection result notified from the detection device; An image quality control device comprising:

10. Controlling the image quality of each area of ​​the image, transmitting the quality controlled image to a detection device that detects information relating to objects within the image; determining image quality of each area of ​​the image to be controlled according to the detection result notified from the detection device; A program that causes a computer to execute a process.