WO2024096463A1 - Dispositif électronique fournissant un service à l'aide d'un calcul en périphérie et son procédé de fonctionnement - Google Patents

Dispositif électronique fournissant un service à l'aide d'un calcul en périphérie et son procédé de fonctionnement Download PDF

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Publication number
WO2024096463A1
WO2024096463A1 PCT/KR2023/016953 KR2023016953W WO2024096463A1 WO 2024096463 A1 WO2024096463 A1 WO 2024096463A1 KR 2023016953 W KR2023016953 W KR 2023016953W WO 2024096463 A1 WO2024096463 A1 WO 2024096463A1
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WIPO (PCT)
Prior art keywords
image
abnormal
electronic device
abnormal behavior
store
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Application number
PCT/KR2023/016953
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English (en)
Korean (ko)
Inventor
김용범
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김용범
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Publication of WO2024096463A1 publication Critical patent/WO2024096463A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/72—Data preparation, e.g. statistical preprocessing of image or video features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/772—Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/98—Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
    • G08B25/14—Central alarm receiver or annunciator arrangements

Definitions

  • This relates to electronic devices that provide services using edge computing and methods of operating the electronic devices.
  • Edge computing is a technology that processes data in real time through small distributed servers rather than centralized servers. Edge computing can mean processing data at the edge of the network, unlike cloud computing where a central server processes all data.
  • Such edge computing is being applied to various technological fields such as autonomous driving and smart factories that require real-time response.
  • a device is provided.
  • Electronic devices installed within the store can quickly detect visitors behaving abnormally.
  • FIG. 1 is a conceptual diagram illustrating the operation of monitoring the behavior of visitors to a store and detecting abnormal behavior using an electronic device within the store, according to an embodiment.
  • Figure 2 is a flowchart showing a method of operating an electronic device that detects abnormal behavior of a visitor through monitoring inside a store, according to an embodiment.
  • Figure 3 is a flowchart showing a method of operating an electronic device that detects an abnormal image, according to an embodiment.
  • Figure 4 is a flowchart showing a method of operating an electronic device that verifies a candidate image, according to an embodiment.
  • FIG. 5 is a diagram illustrating a process for updating an abnormal behavior detection model, according to an embodiment.
  • FIG. 6 is a flowchart illustrating a method of operating an electronic device that generates and provides statistical data based on results of monitoring a store, according to an embodiment.
  • Figure 7 is a block diagram showing the configuration of an electronic device, according to an embodiment.
  • “electronic devices” include mobile phones, smart phones, laptop computers, PCs, desktops, home appliances, tablet PCs, e-book terminals, digital broadcasting terminals, and personal digital assistants (PDAs). ), PMP (Portable Multimedia Player), navigation, MP3 player, digital camera, etc., but is not limited to these.
  • FIG. 1 is a conceptual diagram illustrating an operation of monitoring the behavior of a visitor to a store and detecting abnormal behavior using an electronic device 10 within the store, according to an embodiment.
  • the electronic device 10 can monitor the interior of the store. By monitoring the interior of the store, the electronic device 10 can detect abnormal behavior of visitors to the store. In this case, the electronic device 10 may be installed inside the store.
  • the electronic device 10 has a built-in camera or is connected to a camera, so the electronic device 10 can capture the inside of the store in real time.
  • the monitoring video is transmitted from the electronic device 10 to the server 20, and when the analysis result is provided, the store stores in real time.
  • the server 20 There may be difficulties in monitoring. For example, if there are limitations in network connection, waiting time, bandwidth, etc. between the electronic device 10 and the server 20, the monitoring image can be viewed directly from the electronic device 10 without requesting monitoring image analysis from the server 20.
  • the monitoring image can be viewed directly from the electronic device 10 without requesting monitoring image analysis from the server 20.
  • the electronic device 10 may request detailed analysis from the server 20 for images that are not easy to analyze with the electronic device 10.
  • the server 20 may perform analysis on the requested image and transmit the analysis result to the electronic device 10 as feedback information. Additionally, the server 20 may request confirmation of the analysis result from the manager's electronic device 30.
  • the manager's electronic device 30 may transmit feedback information about the analysis results of the server 20 to the server 20 .
  • the electronic device 10 may transmit monitoring result information to the manager's electronic device 30.
  • the electronic device 10 may receive feedback information about the monitoring result information from the manager's electronic device 30.
  • FIG. 2 is a flowchart showing a method of operating an electronic device 10 that detects abnormal behavior of a visitor through monitoring inside a store, according to an embodiment.
  • the electronic device 10 may acquire a monitoring image by photographing the interior of the store using a camera.
  • the camera in the electronic device 10 can photograph the interior of the store in real time.
  • the electronic device 10 may detect an abnormal image containing the visitor's abnormal behavior based on the monitoring image and the abnormal behavior detection model.
  • the electronic device 10 may download an abnormal behavior detection model from the server 20.
  • the abnormal behavior detection model may be a model that detects preset abnormal behavior in a video.
  • the abnormal behavior detection model can receive a video as input data and output a video of a visitor corresponding to a preset abnormal behavior in the video as output data.
  • the abnormal behavior detection model can be updated by learning the monitoring image acquired within the electronic device 10. Additionally, the electronic device 10 may periodically obtain an updated version of the abnormal behavior detection model from the server 20.
  • the electronic device 10 may detect a candidate image predicted to be abnormal behavior based on an abnormal behavior detection model and determine the candidate image as an abnormal image through verification of the candidate image.
  • the process of detecting candidate images and determining abnormal images is explained in FIG. 3.
  • the electronic device 10 may transmit the abnormal image to the store manager's electronic device 30.
  • the electronic device 10 may transmit notification information indicating that an abnormal image has been detected to the manager's electronic device 30.
  • the electronic device 10 may transmit information on the type of abnormal behavior corresponding to the abnormal image and information on measures to be taken regarding the abnormal behavior to the electronic device 30 of the manager.
  • the electronic device 10 may output warning information within the store as an abnormal image is detected.
  • the electronic device 10 may output warning information corresponding to an abnormal image. Specifically, if the abnormal behavior included in the abnormal image is lying down in the store, the electronic device 10 may output warning information indicating that lying down in the store is prohibited.
  • the electronic device 10 may output first warning information corresponding to abnormal behavior based on the abnormal image and the number of visitors visiting the store. Specifically, when the only visitors visiting the store are those who engage in abnormal behavior, the electronic device 10 may output warning information notifying the prohibition of abnormal behavior and warning of legal action. On the other hand, if the number of visitors to the store exceeds a preset number, the electronic device 10 may output warning information to prohibit abnormal behavior and provide caution.
  • the electronic device 10 may transmit options for countermeasures against abnormal images to the manager's electronic device 30.
  • FIG. 3 is a flowchart showing a method of operating the electronic device 10 for detecting an abnormal image, according to an embodiment.
  • the electronic device 10 may acquire a monitoring image as input data for an abnormal behavior detection model.
  • the electronic device 10 may remove overlapping images from the monitoring image and then obtain the preprocessed monitoring image as input data for an abnormal behavior detection model.
  • step S320 the electronic device 10 may detect a candidate image corresponding to a reference image representing a preset abnormal behavior based on the abnormal behavior detection model.
  • preset abnormal behaviors include lying down in the store, destroying facilities in the store, climbing on facilities in the store, fighting in the store, stealing items in the store, and physically touching the store. It may include at least one of the following actions: exposing the user, and making a fuss in the store, and is not limited to the above examples.
  • a reference image is an image showing preset abnormal behavior.
  • a video that meets the criteria for abnormal behavior that can be judged as abnormal behavior can be used as a reference video.
  • the abnormal behavior detection model may be a learning model that learns a reference image showing abnormal behavior and detects whether the input image is an image showing abnormal behavior based on the learning results.
  • the abnormal behavior detection model may be downloaded in advance from the server 20 and may be updated periodically.
  • the electronic device 10 may verify the candidate image based on the matching level of the candidate image with respect to the reference image.
  • the matching level may indicate the degree to which a candidate image matches a reference image. Therefore, the higher the matching level, the more similar the candidate image may be to the reference image.
  • the candidate image may be determined to be similar to the reference image, so the electronic device 10 can immediately detect the candidate image as an abnormal image.
  • the electronic device 10 may verify the candidate image based on the server 20 or the manager's management device. there is. The process of verifying the candidate image based on the matching level of the candidate image is explained in FIG. 4.
  • step S340 if the candidate image is verified, the electronic device 10 may detect the candidate image as an abnormal image.
  • the electronic device 10 may determine the candidate image as an abnormal image and transmit the abnormal image to the manager's electronic device 30. Additionally, the electronic device 10 may transmit notification information notifying the visitor's abnormal behavior to the manager's electronic device 30.
  • FIG. 4 is a flowchart showing a method of operating the electronic device 10 for verifying a candidate image, according to an embodiment.
  • the electronic device 10 may obtain the matching level of the candidate image with respect to the reference image.
  • the electronic device 10 may check whether the matching level of the candidate image is low level. For example, if the matching level of the candidate image is low level, the electronic device 10 may perform verification of the candidate image through the server 20 in step S421. On the other hand, if the matching level of the candidate image is high level, the electronic device 10 can verify the candidate image as an abnormal image in step S430 without going through the server 20. In other words, there is an effect that abnormal behavior can be monitored in real time on the electronic device 10 without transmitting the candidate image detected by the electronic device 10 to the server 20.
  • the electronic device 10 may request verification of the candidate image from the server 20.
  • the server 20 can check whether the candidate video contains abnormal behavior of the visitor.
  • the server 20 may determine whether the candidate image is an abnormal image using a higher version model than the abnormal behavior detection model transmitted to the electronic device 10.
  • the server 20 may transmit the determined result to the electronic device 10 as feedback information.
  • the server 20 may transmit the verification result of the candidate image to the manager's electronic device 30 and request confirmation of the verification result.
  • the manager's electronic device 30 may display a screen for requesting feedback information about the candidate image. For example, the manager's electronic device 30 may display a message requesting confirmation whether the visitor's behavior in the candidate video is abnormal behavior. The manager's electronic device 30 may display an icon to select whether the behavior is abnormal. Additionally, the manager's electronic device 30 may display an icon that registers the behavior in the candidate video as abnormal behavior. For example, the manager's electronic device 30 may receive an input for selecting an icon indicating that the visitor's behavior in the candidate video is abnormal behavior. That is, the manager's electronic device 30 may receive feedback information about the candidate image and transmit the feedback information to the server 20.
  • step S424 the server 20 may obtain feedback information about the candidate image directly or from the manager's electronic device 30.
  • the server 20 may transmit feedback information to the electronic device 10.
  • the electronic device 10 may verify the candidate image based on the feedback information. For example, when verification is completed that the candidate image is an abnormal image, the electronic device 10 may determine the candidate image as an abnormal image and perform the next process according to the detection of the abnormal image. On the other hand, when verification is completed that the candidate image is not an abnormal image, the electronic device 10 may determine that the candidate image is a normal image.
  • the electronic device 10 may use the results of verifying the candidate image as learning data.
  • the electronic device 10 may learn a candidate image and information indicating whether the candidate image is an abnormal image, and update the abnormal behavior detection model based on the learning result.
  • FIG. 5 is a diagram illustrating a process for updating an abnormal behavior detection model, according to an embodiment.
  • the server 20 may transmit an abnormal behavior detection model to the electronic device 10.
  • the electronic device 10 can receive an abnormal behavior detection model and monitor the store in real time using the abnormal behavior detection model.
  • the manager's electronic device 30 may transmit reference information about abnormal behavior to the electronic device 10 within the store.
  • the electronic device 10 may update the abnormal behavior detection model based on reference information input from the manager's electronic device 30.
  • the electronic device 10 may obtain reference information about abnormal behavior.
  • the reference information may include a type of abnormal behavior and at least one behavior that can be determined to be similar to the abnormal behavior.
  • the electronic device 10 may generate a reference image based on reference information.
  • the electronic device 10 may generate a reference image that can be judged as abnormal behavior within the store based on the structure and reference information within the store.
  • the reference image may be obtained from an image captured within the store or may be obtained from a virtually generated image.
  • the electronic device 10 may update the abnormal behavior detection model based on the result of learning the reference image.
  • the electronic device 10 may provide an abnormal behavior detection model suitable for the store by updating the abnormal behavior detection model based on reference information input by the manager.
  • FIG. 6 is a flowchart showing a method of operating the electronic device 10 that generates and provides statistical data based on the results of monitoring a store, according to an embodiment.
  • the electronic device 10 may generate statistical data on preset abnormal behavior based on the results of monitoring the store.
  • the electronic device 10 may generate statistical data about the type of at least one abnormal behavior that occurred during a preset period and the time when the at least one abnormal behavior occurred.
  • the electronic device 10 may obtain an analysis result of analyzing status information in the store corresponding to a time when at least one abnormal behavior did not occur.
  • the electronic device 10 compares the state information of the store at the time when the abnormal behavior occurred with the state information of the store at the time when the abnormal behavior did not occur, and provides information about the cause or environment in which the abnormal behavior occurred based on the comparison result. can be obtained.
  • step S620 the electronic device 10 may transmit statistical data to the manager's electronic device 30.
  • FIG. 7 is a block diagram showing the configuration of the electronic device 10, according to one embodiment.
  • the electronic device 10 may include a camera 710, a communication device 720, a memory 730, and a processor 740.
  • the electronic device 10 may include more components than those shown in FIG. 7 . Additionally, the electronic device 10 may include fewer components than those shown in FIG. 7 .
  • the camera 710 can obtain an image or video by photographing a predetermined object.
  • the camera 710 can photograph a predetermined space in real time.
  • the camera 710 can obtain monitoring images by filming the inside of the store in real time.
  • the communication device 720 can communicate with an external device.
  • the communication device 720 may include at least one of a Wi-Fi chip, a Bluetooth chip, an NFC chip, and a wireless communication chip.
  • the processor 740 can communicate with various external devices using the communication device 720.
  • the memory 730 may store a plurality of applications or applications running on the electronic device 10, data for operating the electronic device 10, and commands.
  • the processor 740 controls the overall operation of the electronic device 10 and may include at least one processor such as a CPU.
  • the processor 740 may obtain a monitoring image by photographing the interior of the store through the camera 710.
  • the processor 740 may detect an abnormal image containing the visitor's abnormal behavior based on the monitoring image and the abnormal behavior detection model.
  • the processor 740 may download the abnormal behavior detection model from the server 20.
  • the abnormal behavior detection model may be a model that detects preset abnormal behavior in a video.
  • the processor 740 may detect a candidate image predicted to be abnormal behavior based on an abnormal behavior detection model and determine the candidate image as an abnormal image through verification of the candidate image.
  • the processor 740 may acquire the monitoring image as input data for an abnormal behavior detection model. For example, the processor 740 may remove overlapping images from the monitoring image and then obtain the preprocessed monitoring image as input data for an abnormal behavior detection model.
  • the processor 740 may detect a candidate image corresponding to a reference image representing a preset abnormal behavior based on the abnormal behavior detection model.
  • preset abnormal behaviors include lying down in the store, destroying facilities in the store, climbing on facilities in the store, fighting in the store, stealing items in the store, and physically touching the store. It may include at least one of the following actions: exposing the user, and making a fuss in the store, and is not limited to the above examples.
  • a reference image is an image showing preset abnormal behavior.
  • a video that meets the criteria for abnormal behavior that can be judged as abnormal behavior can be used as a reference video.
  • the abnormal behavior detection model may be a learning model that learns a reference image showing abnormal behavior and detects whether the input image is an image showing abnormal behavior based on the learning results.
  • the abnormal behavior detection model can be analyzed through hardware processes (CPU, GPU, memory, etc.) by installing a dedicated application in the electronic device.
  • a dedicated application in the electronic device.
  • the processor 740 may verify the candidate image based on the matching level of the candidate image with respect to the reference image.
  • the matching level may indicate the degree to which a candidate image matches a reference image. Therefore, the higher the matching level, the more similar the candidate image may be to the reference image.
  • the processor 740 may request verification of the candidate image from the server 20.
  • the server 20 can check whether the candidate video contains abnormal behavior of the visitor.
  • the server 20 may determine whether the candidate image is an abnormal image using a higher version model than the abnormal behavior detection model transmitted to the electronic device 10.
  • the processor 740 may perform verification of the candidate image through the server 20.
  • the electronic device 10 can verify the candidate image as an abnormal image without going through the server 20. That is, abnormal behavior can be monitored in real time on the electronic device 10 without transmitting the candidate image detected by the electronic device 10 to the server 20.
  • the processor 740 may verify the candidate image based on feedback information obtained from the server 20 or the manager's electronic device 30. For example, when verification is completed that the candidate image is an abnormal image, the processor 740 may determine the candidate image to be an abnormal image. On the other hand, if verification is completed that the candidate image is not an abnormal image, the processor 740 may determine that the candidate image is a normal image.
  • the processor 740 may use the results of verifying the candidate images as learning data.
  • the processor 740 may learn a candidate image and information indicating whether the candidate image is an abnormal image, and update the abnormal behavior detection model based on the learning result.
  • the processor 740 may update the abnormal behavior detection model based on reference information input from the manager's electronic device 30. Specifically, the processor 740 may obtain reference information about abnormal behavior.
  • the reference information may include a type of abnormal behavior and at least one behavior that can be determined to be similar to the abnormal behavior.
  • the processor 740 may generate a reference image based on reference information.
  • the processor 740 may generate a reference image that can be judged as abnormal behavior within the store based on the structure and reference information within the store.
  • the reference image may be obtained from an image captured within the store or may be obtained from a virtually generated image.
  • the processor 740 may update the abnormal behavior detection model based on the results of learning the reference image.
  • the processor 740 may provide an abnormal behavior detection model suitable for the store by updating the abnormal behavior detection model based on reference information input by the manager.
  • the processor 740 may determine the candidate image as an abnormal image and transmit the abnormal image to the manager's electronic device 30. Additionally, the processor 740 may transmit notification information notifying the visitor's abnormal behavior to the manager's electronic device 30 through the communication device 720.
  • the processor 740 may transmit information on the type of abnormal behavior corresponding to the abnormal image and information on measures to be taken regarding the abnormal behavior to the manager's electronic device 30 through the communication device 720 .
  • the processor 740 may output warning information within the store. For example, the processor 740 may output warning information corresponding to an abnormal image. Specifically, if the abnormal behavior included in the abnormal image is lying down in the store, the electronic device 10 may output warning information indicating that lying down in the store is prohibited.
  • the processor 740 may output first warning information corresponding to abnormal behavior based on the abnormal video and the number of visitors visiting the store. Additionally, the processor 740 may transmit options for countermeasures against abnormal images to the manager's electronic device 30 through the communication device 720.
  • the above-described method of operating the electronic device and server may be implemented in the form of a computer-readable storage medium that stores instructions or data executable by a computer or processor.
  • Such computer-readable storage media may include ROM, RAM, hard disk, solid-state disk (SSD), and store instructions or software, related data, data files, and data structures.
  • the present invention can be used in the field of electronic devices that provide services using edge computing and methods of operating electronic devices.

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Abstract

La présente invention concerne un dispositif électronique fournissant un service à l'aide d'un calcul en périphérie et son procédé de fonctionnement. Un procédé de fonctionnement d'un dispositif électronique fournissant un service de gestion de magasin peut comprendre les étapes consistant à : obtenir une vidéo de surveillance par capture de l'intérieur d'un magasin à l'aide d'une caméra ; détecter une vidéo anormale contenant un comportement anormal d'un visiteur sur la base de la vidéo de surveillance et d'un modèle de détection de comportement anormal ; transmettre la vidéo anormale au dispositif électronique du gestionnaire du magasin ; et délivrer en sortie des informations d'avertissement à l'intérieur du magasin lorsque la vidéo anormale est détectée.
PCT/KR2023/016953 2022-10-31 2023-10-30 Dispositif électronique fournissant un service à l'aide d'un calcul en périphérie et son procédé de fonctionnement WO2024096463A1 (fr)

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KR1020220142381A KR20240061109A (ko) 2022-10-31 2022-10-31 엣지 컴퓨팅을 이용한 서비스를 제공하는 전자 장치 및 그의 동작 방법
KR10-2022-0142381 2022-10-31

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KR102234555B1 (ko) * 2020-10-23 2021-03-31 주식회사 만랩 무인 매장 운영을 위한 방범 및 보안 관리 방법, 장치 및 시스템
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KR20220049828A (ko) * 2020-10-15 2022-04-22 주식회사 넷온 무인결제시스템
KR20220057213A (ko) * 2020-10-29 2022-05-09 주식회사 아디아랩 인공지능 기반의 이상행동 감지 시스템
KR20220084762A (ko) * 2020-12-14 2022-06-21 주식회사 에스원 무인 매장용 블랙리스트 등록 방법 및 이를 이용한 블랙 리스트 등록 시스템

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20220000226A (ko) * 2020-06-25 2022-01-03 주식회사 자비스넷 엣지 컴퓨팅 기반 지능형 보안 감시 서비스 제공 시스템
KR20220049828A (ko) * 2020-10-15 2022-04-22 주식회사 넷온 무인결제시스템
KR102234555B1 (ko) * 2020-10-23 2021-03-31 주식회사 만랩 무인 매장 운영을 위한 방범 및 보안 관리 방법, 장치 및 시스템
KR20220057213A (ko) * 2020-10-29 2022-05-09 주식회사 아디아랩 인공지능 기반의 이상행동 감지 시스템
KR20220084762A (ko) * 2020-12-14 2022-06-21 주식회사 에스원 무인 매장용 블랙리스트 등록 방법 및 이를 이용한 블랙 리스트 등록 시스템

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