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WO2023093978A1 - Procédé de surveillance d'une zone de surveillance, système de surveillance, programme informatique et support de stockage - Google Patents

Procédé de surveillance d'une zone de surveillance, système de surveillance, programme informatique et support de stockage Download PDF

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Publication number
WO2023093978A1
WO2023093978A1 PCT/EP2021/082752 EP2021082752W WO2023093978A1 WO 2023093978 A1 WO2023093978 A1 WO 2023093978A1 EP 2021082752 W EP2021082752 W EP 2021082752W WO 2023093978 A1 WO2023093978 A1 WO 2023093978A1
Authority
WO
WIPO (PCT)
Prior art keywords
surveillance area
surveillance
elements
data
monitoring
Prior art date
Application number
PCT/EP2021/082752
Other languages
English (en)
Inventor
Mark DEN HARTOG
Goran Dozic
Original Assignee
Robert Bosch Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Robert Bosch Gmbh filed Critical Robert Bosch Gmbh
Priority to PCT/EP2021/082752 priority Critical patent/WO2023093978A1/fr
Publication of WO2023093978A1 publication Critical patent/WO2023093978A1/fr

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
    • G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
    • G08B13/19665—Details related to the storage of video surveillance data
    • G08B13/19671—Addition of non-video data, i.e. metadata, to video stream
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms
    • G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
    • G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
    • G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
    • G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
    • G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction

Definitions

  • the invention describes a method for monitoring a surveillance area, wherein the surveillance area is monitored with at least one camera, especially with a plurality of cameras.
  • monitoring of surveillance areas is widely used in different technical fields, for example monitoring of stores or company buildings as well as CCTV. Therefore, cameras are arranged to monitor at least a part of the surveillance area and to provide image data. The image data are analysed by a security officer or software applications.
  • the document US 2016/0182818 discloses a video capturing system comprising a composition map with attribute information of objects.
  • the invention concerns a method for monitoring of a surveillance area with the features of claim 1. Furthermore, the invention concerns a surveillance system, a computer program and a storage medium. Preferred and advantageous embodiments are disclosed in the sub claims, the description and the attached figures.
  • the invention is directed to a method for monitoring of a surveillance area.
  • the method is preferably for computer implementation and/or software implementation.
  • the method is adapted and/or configured for monitoring and/or surveillance of the surveillance area.
  • Monitoring and/or surveillance is especially understood as a video and/or picture based monitoring and/or surveillance.
  • the surveillance area is monitored for detecting objects, persons and/or events, for example detecting an emergency or fire.
  • the method is for tracking an object, person or event.
  • the surveillance area could be an indoor and/or outdoor area.
  • the surveillance area is a three-dimensional area and/or a fragmented area, like a plurality of buildings or rooms.
  • the surveillance area is monitored with at least one camera, preferably with a plurality of cameras e.g. more than 10 or 50 cameras.
  • the cameras are stationary and/or movable cameras, for example, the cameras are mounted and/or fixed on a wall, wherein the cameras are especially pan, tilt, zoom cameras.
  • the camera or cameras are adapted as movable cameras, like mounted on a robot or a drone.
  • the camera is adapted and/or configured to record or take image data.
  • the camera is taking and/or recording the image data for a recording area which is for example defined by the field of view of the camera.
  • the recording area is especially a subset of the surveillance area.
  • the image data comprises at least an image and/or a video showing the recording area.
  • the recording area and/or the surveillance area is monitored by an additional sensor, for example a microphone, lightening sensor, temperature sensor or any other sensor for measuring the physical quantity.
  • the camera and/or the cameras are adapted and/or configured to move their recording area, especially within the surveillance area, for example in order to monitor the whole surveillance area by moving the recording area.
  • the monitoring area is partitioned, divided and/or separated into surveillance area elements.
  • the surveillance area elements are also called elements.
  • the complete surveillance area is partitioned and/or divided by the surveillance area elements.
  • the surveillance area could be partitioned regularly or irregularly, using surveillance area elements of same or different size and/or geometry.
  • the surveillance area is with other words granulated and/or meshed by the surveillance area elements.
  • an element vector is computed, determined and/or to each of the surveillance area elements an element vector is assigned.
  • the element vector may be adapted as data collection.
  • the elements of the vector have arbitrary structure, type and/or size. This allows for example to have element vectors containing a collection of data of different kinds.
  • the element vector allows to annotate space with various information types, e.g. strings, numbers, lists, matrixes and/or complex structures.
  • the element vector comprises a priority information for the concerning surveillance area element and meta data for the concerning surveillance area element.
  • the priority information describes and/or defines a monitoring priority for the surveillance area element.
  • the monitoring priority is based on and/or describing the importance and/or priority of monitoring and/or recording image data for the surveillance area element.
  • the priority information and/or monitoring priority comprises the priorities and/or categories “low, medium and high”.
  • a high priority information and/or monitoring priority means for example that this surveillance area element should be monitored more regularly and/or more often than surveillance area elements with medium priority information and/or monitoring priority, wherein a medium priority information and/or monitoring priority means especially that this surveillance area element should be monitored more often than surveillance area elements with low priority information and/or monitoring priority.
  • the meta data comprise information indicating, describing and/or based on how the monitoring of the surveillance and/or the image data of the concerning surveillance area element should be updated and/or interpreted.
  • the information how to update describes for example how information or data regarding the surveillance area element and/or components of the element vector should be updated and/or changed, especially based on historic and/or new image data or monitoring results of one or more cameras. How to interpret the monitoring, surveillance and/or image data describe for example how to analyse the surveillance area element, how to use analyse results and/or how to handle it.
  • This invention is based on the idea to propose a method for detecting and representing the observational characteristics of an area observed by surveillance cameras, by creating an annotation map of the area, indicating areas of high/low/no interest, combined with real-time observations, from all camera/devices in the network. This can be used to raise alarms, direct the paths of moving cameras/drones and/or to dispatch a service officer.
  • the surveillance area elements are two-dimensional space elements or three-dimensional volume elements.
  • two-dimensional space elements are flat or curved area elements, having a regular or irregular shape, for example triangular, rectangular or polygonal.
  • Three-dimensional volume elements are for example voxels, cubes or complex shaped.
  • the surveillance area elements are dividing the surveillance area regularly or irregularly.
  • the surveillance area elements may have a same or different shape and/or size.
  • the two-dimensional space elements divide for example a plan (e.g. a building map or floor map).
  • the surveillance area elements are dividing and partitioned the recorded area and/or image area of the surveillance area monitored by the cameras.
  • the surveillance area is described by a three-dimensional model and divided by three-dimensional volume elements.
  • the priority information include and/or define a time between two observations, especially a time between two direct observations.
  • the direct observation could be a direct information of one camera.
  • a direct observation is for example a recording of image data by a camera having a direct and non-obstructed sight on the surveillance area element.
  • the priority information include and/or describe a frequency for the observation of the surveillance area element, especially a frequency of direct and/or nonobstructed observation.
  • the priority information include and/or describe in a preferred embodiment timestamps for the observation of the surveillance area element, for example timestamps for the camera and/or different cameras of the plurality of the cameras. This embodiment is based on the idea to use the element vector for executing, organisation and/or structuring the monitoring, surveillance and/or image recording.
  • the priority information include and/or describe observation parameters for the surveillance area element.
  • the observation parameter comprise, include and/or describe for example a physical observation characteristic, like a camera focus or zoom. Additionally and/or alternatively, the observation parameter include, comprise and/or describe an optical resolution, for example a needed optical resolution for recording the image data of the surveillance area element or which dynamic range or noise is allowed.
  • the observation parameter comprise, describe and/or include an object type and/or an object ID for the object located and/or shown by the surveillance area element.
  • the observation parameter comprise a direction for direct observation, for example the preferred direction for recording the image. For example the direction for direct observation defines that surveillance area elements should be monitored using a defined angle and/or view.
  • the meta data comprise monitoring results.
  • the monitoring results in the meta data are concerning and/or based on the surveillance of the surveillance area element.
  • the monitoring results are based and/or comprise for example observations, monitoring, image data and/or analyse results of the image data of one or more cameras monitoring the concerning surveillance area element.
  • the monitoring results comprise for example a parameter determined based on processing and/or analysing the image data, like a parameter, object type, object class, optical flux or others.
  • the parameter, monitoring result and/or image data may be statistically or mathematically processed, like determining its mean value and/or standard deviation, wherein the statistics may be done for a time interval or over different cameras.
  • the meta data comprise, include and/or describe surveillance data.
  • the surveillance data are for the surveillance area element of the element vector.
  • the surveillance data are based on image data of at least one of the cameras, sensor data taken by a sensor linked to the surveillance area element and/or data based on human observation of the surveillance area element.
  • the surveillance data are determined as and/or based on an analysis of the image data of a moving camera, a drone observation, the sensor data and/or the human observation.
  • the surveillance data describe an object type, motion and/or verification information of a human observer.
  • the sensor for taking the sensor data is for example a motion detector, a door sensor or a fire detector.
  • the meta data comprise predictive data.
  • the predictive data are based on a scene context and/or describe a scene context.
  • the predictive data are for example determined using data analyses, scene context and/or machine learning algorithms.
  • the predictive data are generated based on predictions from the scene context, e.g. when a train passes, it is assumed that between the train and a wall immediately behind the train no further object can be located.
  • a dynamic object in the surveillance area and/or recording area is determined, especially determined based on the image data.
  • an object area is determined, wherein the object area is for example and two-dimensional area in the image plane or the surveillance area.
  • the object area is a volume in a three-dimensional space like the surveillance area.
  • elements showing or belonging to the dynamic object supplementary data are determined.
  • the supplementary data are for example comprising an object type, an object class, an object ID, an object movement, velocity or kinematic information.
  • the supplementary data include a distance, 3-D information, position and/or orientation.
  • the element vector of a surveillance area element belonging to the dynamic object and/or the object area comprises the supplementary data.
  • the supplementary data are for example acquired by direct observation, e.g. a time of flight sensor, triangulation, floor sensors, ultrasound or lead our sensors.
  • the supplementary data are acquired using heuristic methods and/or algorithms like video analytics, artificial intelligence and/or other appropriate methods of interfering or measuring spatial and/or geometric information of the object.
  • the object area is divided and/or partitioned into object elements.
  • the object elements are two-dimensional or three-dimensional elements, especially like the surveillance area elements.
  • an object vector is determined and/or assigned to it.
  • the object vector is especially similarly constructed and/or concepted as the element vector.
  • the object vector comprises an observation priority level and/or object data.
  • the observation priority level describes a level of priority when monitoring and/or recording image data for the object or a section of the object.
  • the object is showing a person of interest, wherein the observation priority level describes that object elements of this objects, e.g. showing the face of the person, should be monitored and/or recorded all the time and/or with high priority.
  • the object data comprise observation results, observation parameter, object type, object class and/or object ID. This embodiment is based on the idea that for dynamic objects a quantity like the element vectors could be defined and/or calculated, whereby this quantity is called the object vector.
  • An further object of the invention concerns a surveillance system for monitoring of an surveillance area, wherein the surveillance area is monitored with at least one camera, wherein the camera is configured to provide image data, with a processing module, wherein the processing module is adapted and/or configured to partition the surveillance area into surveillance area elements and/or a partitioning of the surveillance area into surveillance area elements is provided to the processing module, wherein the processing module is adapted and/or configured to determine and/or assign element vectors for/to the surveillance area elements, wherein the wherein the element vector comprises a priority information and metadata, wherein the priority information describes a monitoring priority of for the surveillance area element, wherein the metadata comprises information indicating how the monitoring and/or image data should be updated and/or interpreted.
  • the invention concerns a computer program, wherein the computer program is configured and/or adapted for execution, implementation and/or usage on the surveillance system and/or on a computer, wherein the computer program is adapted and/or configured to execute the method for monitoring the surveillance area.
  • the invention also concerns a storage medium, wherein the computer program is stored on the storage medium.
  • Figure 1 an example for monitoring a surveillance area.
  • Figure 1 shows a room 1, wherein in the room 1 cameras 2a, b, c, d are mounted.
  • the cameras 2a, b, c, d are adapted and/or configured to take images of recording areas 3a, b, c, d and provide them as image data.
  • the cameras 2a, b, c, d are part of a surveillance system, wherein the surveillance system is adapted and/or configured for monitoring of a surveillance area 4.
  • the surveillance area 4 is a subsection of the room 1.
  • the surveillance area 4 is monitored and/or covered by the recording areas 3a, b, c, d of the cameras 2a, b, c, d.
  • the surveillance area is partitioned and/or divided by surveillance area elements 5.
  • the surveillance area elements 5 are adapted as triangles and forming a mesh for the surveillance are 4.
  • the surveillance system comprises a processing module, wherein the processing module is adapted and/or configured to determine an element vector for each of the surveillance area elements 5 and/or to assign an element vector to each of the surveillance area elements 5.
  • the element vector comprises a priority information for the assigned surveillance area element, for example defining how often and/or regularly the surveillance area element has to be monitored by the cameras 2a, b, c, d.
  • the cameras 2a, b, c, d have a movable recording area 3a, b, c, d and the cameras 2a, b, c, d have to be steered to take image data of different or specific surveillance area elements, wherein the priority information sets a minimum time interval or frequency for taking pictures of the surveillance area element 5.
  • the element vector also comprises metadata for the surveillance area element 5, wherein the metadata comprise information of how to monitor the surveillance area element 5 and/or how image data of this surveillance area element 5 have to be interpreted.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Library & Information Science (AREA)
  • Closed-Circuit Television Systems (AREA)

Abstract

La présente invention concerne un procédé de surveillance d'une zone de surveillance (4), la zone de surveillance (4) étant surveillée au moyen d'au moins un appareil de prise de vues (2a, b, c, d), l'appareil de prise de vues (2a, b, c, d) enregistrant des données d'image, la zone de surveillance (4) étant divisée en éléments (5) de la zone de surveillance (4), pour chaque élément (5) de la zone de surveillance (4) un vecteur d'élément étant déterminé et/ou affecté, le vecteur d'élément comprenant des informations de priorité et des métadonnées, les informations de priorité décrivant une priorité de surveillance pour l'élément (5) de la zone de surveillance (4), les métadonnées comprenant des informations indiquant la manière dont les données de surveillance et/ou d'image doivent être mises à jour et/ou interprétées.
PCT/EP2021/082752 2021-11-24 2021-11-24 Procédé de surveillance d'une zone de surveillance, système de surveillance, programme informatique et support de stockage WO2023093978A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/EP2021/082752 WO2023093978A1 (fr) 2021-11-24 2021-11-24 Procédé de surveillance d'une zone de surveillance, système de surveillance, programme informatique et support de stockage

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP2021/082752 WO2023093978A1 (fr) 2021-11-24 2021-11-24 Procédé de surveillance d'une zone de surveillance, système de surveillance, programme informatique et support de stockage

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WO2023093978A1 true WO2023093978A1 (fr) 2023-06-01

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116416579A (zh) * 2023-06-12 2023-07-11 广东德燊科技有限公司 一种园区安防监控存储信息智能分析管理系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6985172B1 (en) * 1995-12-01 2006-01-10 Southwest Research Institute Model-based incident detection system with motion classification
WO2007094802A2 (fr) * 2005-03-25 2007-08-23 Intellivid Corporation sélection de caméras et suivi d'objets intelligents
US20120002056A1 (en) * 2010-06-30 2012-01-05 Ajou University Industry-Academic Corporation Foundation Apparatus and method for actively tracking multiple moving objects using a monitoring camera
US8922649B2 (en) * 2007-08-29 2014-12-30 Lg Electronics Inc. Method and apparatus for processing video frame
US20160182818A1 (en) 2013-12-27 2016-06-23 Panasonic Intellectual Property Management Co., Ltd. Video capturing apparatus, video capturing system and video capturing method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6985172B1 (en) * 1995-12-01 2006-01-10 Southwest Research Institute Model-based incident detection system with motion classification
WO2007094802A2 (fr) * 2005-03-25 2007-08-23 Intellivid Corporation sélection de caméras et suivi d'objets intelligents
US8922649B2 (en) * 2007-08-29 2014-12-30 Lg Electronics Inc. Method and apparatus for processing video frame
US20120002056A1 (en) * 2010-06-30 2012-01-05 Ajou University Industry-Academic Corporation Foundation Apparatus and method for actively tracking multiple moving objects using a monitoring camera
US20160182818A1 (en) 2013-12-27 2016-06-23 Panasonic Intellectual Property Management Co., Ltd. Video capturing apparatus, video capturing system and video capturing method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116416579A (zh) * 2023-06-12 2023-07-11 广东德燊科技有限公司 一种园区安防监控存储信息智能分析管理系统
CN116416579B (zh) * 2023-06-12 2023-08-18 广东德燊科技有限公司 一种园区安防监控存储信息智能分析管理系统

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