WO2020114138A1 - Information associated analysis method and apparatus, and storage medium and electronic device - Google Patents
Information associated analysis method and apparatus, and storage medium and electronic device Download PDFInfo
- Publication number
- WO2020114138A1 WO2020114138A1 PCT/CN2019/112925 CN2019112925W WO2020114138A1 WO 2020114138 A1 WO2020114138 A1 WO 2020114138A1 CN 2019112925 W CN2019112925 W CN 2019112925W WO 2020114138 A1 WO2020114138 A1 WO 2020114138A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- information
- target object
- behavior
- surveillance video
- relevant information
- Prior art date
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 claims abstract description 39
- 230000006399 behavior Effects 0.000 claims description 64
- 238000010219 correlation analysis Methods 0.000 claims description 23
- 238000004590 computer program Methods 0.000 claims description 22
- 230000015654 memory Effects 0.000 claims description 19
- 238000012544 monitoring process Methods 0.000 claims description 14
- 230000001815 facial effect Effects 0.000 claims description 11
- 239000013598 vector Substances 0.000 claims description 11
- 238000013527 convolutional neural network Methods 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 8
- 230000002776 aggregation Effects 0.000 claims description 4
- 238000004220 aggregation Methods 0.000 claims description 4
- 238000012097 association analysis method Methods 0.000 claims description 4
- 238000005034 decoration Methods 0.000 claims description 3
- 238000004141 dimensional analysis Methods 0.000 abstract 1
- 238000011160 research Methods 0.000 description 19
- 238000010586 diagram Methods 0.000 description 8
- 230000002265 prevention Effects 0.000 description 7
- 230000005540 biological transmission Effects 0.000 description 6
- 230000002596 correlated effect Effects 0.000 description 6
- 239000000523 sample Substances 0.000 description 6
- 238000013473 artificial intelligence Methods 0.000 description 5
- 230000003542 behavioural effect Effects 0.000 description 5
- 230000004927 fusion Effects 0.000 description 5
- 230000008447 perception Effects 0.000 description 5
- 230000003068 static effect Effects 0.000 description 5
- 230000007704 transition Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000007499 fusion processing Methods 0.000 description 3
- 230000005021 gait Effects 0.000 description 3
- 230000001960 triggered effect Effects 0.000 description 3
- 238000012800 visualization Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 238000007418 data mining Methods 0.000 description 2
- 239000012634 fragment Substances 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012731 temporal analysis Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 208000003464 asthenopia Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000011176 pooling Methods 0.000 description 1
- 230000036544 posture Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- 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
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
-
- 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
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/06—Alarms for ensuring the safety of persons indicating a condition of sleep, e.g. anti-dozing alarms
Definitions
- the invention relates to the technical field of intelligent monitoring, in particular, to an information correlation analysis method and device, a storage medium, and an electronic device.
- Embodiments of the present invention provide an information correlation analysis method and device, a storage medium, and an electronic device, to at least solve the problem of time-consuming and laborious manual browsing of surveillance video in the related art, and research and judgment from a single dimension results in low accuracy.
- an information correlation analysis method which includes: extracting relevant information of a target object from a surveillance video, wherein the relevant information of the target object includes: body characteristic information, behavior characteristic information, Equipment information; multi-dimensional correlation analysis is performed on the extracted relevant information of the target object to obtain a character portrait and behavior track of the target object.
- the method before extracting the relevant information of the target object from the monitoring video, the method further includes: collecting a monitoring video containing the target object, wherein the monitoring video includes at least one of the following image elements: the target The subject himself, the vehicle associated with the target object, the iconic item associated with the target object.
- extracting the relevant information of the target object from the surveillance video includes: extracting the relevant information of the target object from the surveillance video using a portrait multi-dimensional model, wherein the portrait multi-dimensional model is a model built using feature vectors of multiple dimensions of the human body ,
- the portrait multi-dimensional model uses a convolutional neural network to extract features from the image.
- the relevant information of the target object is extracted from the surveillance video, including one of the following: the body characteristic information of the target object is extracted from the surveillance video, wherein the body characteristic information includes at least one of the following: gender, height, hair accessories , Clothing, carry-on items, walking patterns, facial features, head shape, facial decorations; extract the behavioral feature information of the target object from the surveillance video, wherein the behavioral feature information includes at least one of the following: cross-border behavior, activity Area, wandering area, aggregation behavior; extract target equipment information from surveillance video, where the equipment information includes at least one of the following: vehicle type, license plate, vehicle color, model, brand, car sticker, car Accessories information, the handheld mobile terminal of the target object.
- the body characteristic information includes at least one of the following: gender, height, hair accessories , Clothing, carry-on items, walking patterns, facial features, head shape, facial decorations
- extract the behavioral feature information of the target object includes at least one of the following: cross-border behavior, activity Area, wandering area, aggregation behavior
- the method before performing multi-dimensional correlation analysis on the extracted relevant information of the target object, includes: acquiring position information of the handheld mobile terminal of the target object according to signal positioning.
- performing multi-dimensional correlation analysis on the extracted relevant information of the target object includes: associating at least two types of information in the following relevant information of the target object: physical characteristic information, behavior characteristic information, equipment information, The location information of the handheld mobile terminal; comprehensive analysis of the relevant information after the association in time, space, region, trajectory and social relationship.
- the method further includes: predicting the future behavior of the target object according to the result of the comprehensive analysis; and/or The result of the comprehensive analysis is to track the position of the target object.
- the method further comprises: displaying at least one of the following information on the display screen: character portrait generation information, space-time retrospective display information, path tracking information, behavior habit model, security situation map, and real-time portrait monitoring information.
- an information correlation analysis device including: an extraction module for extracting related information of a target object from a surveillance video using a portrait multi-dimensional model, wherein the target object
- the related information includes: physical characteristic information, behavior characteristic information, and equipment information; an analysis module is used to perform multi-dimensional correlation analysis on the extracted related information of the target object to obtain a character portrait and a behavior track of the target object.
- a storage medium in which a computer program is stored, wherein the computer program is configured to execute the steps in any one of the above method embodiments during runtime .
- an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute The steps in any of the above method embodiments.
- the multi-dimensional related information of the target object is extracted from the surveillance video, including body characteristic information, behavior characteristic information, equipment information, etc.; the multi-dimensional related information of the extracted target object is multi-dimensionally correlated Analyze to get the portrait and behavior trajectory of the target object.
- the static data is built with "people" as the core
- the dynamic data formed with various intelligent perception systems are interlinked, integrated, correlated, comprehensively analyzed and judged to form an integrated combat system capable of intelligent discovery, intelligent research and judgment, intelligent triggering, and intelligent tracking. With this as the core support, it can truly form a transition from passive disposal to active discovery, prediction, prevention and early warning.
- FIG. 1 is a block diagram of a hardware structure of a mobile terminal of an information association analysis method according to an embodiment of the present invention
- FIG. 2 is a flowchart of a method for analyzing association of information according to an embodiment of the present invention
- FIG. 3 is a schematic diagram of characteristics of acquiring relevant information of a target object according to an embodiment of the present invention.
- FIG. 4 is a schematic diagram of multi-dimensional model construction and information research and judgment according to an embodiment of the present invention.
- FIG. 5 is a structural block diagram of an information association analysis device according to an embodiment of the present invention.
- FIG. 1 is a block diagram of a hardware structure of a mobile terminal of an information association analysis method according to an embodiment of the present invention.
- the mobile terminal 10 may include one or more (only one is shown in FIG. 1) processor 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc. ) And a memory 104 for storing data.
- the above mobile terminal may further include a transmission device 106 for communication functions and an input/output device 108.
- FIG. 1 is merely an illustration, which does not limit the structure of the mobile terminal described above.
- the mobile terminal 10 may also include more or fewer components than those shown in FIG. 1, or have a different configuration from that shown in FIG.
- the memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for acquiring data information in the embodiment of the present invention, and the processor 102 runs the computer program stored in the memory 104, thereby Implementation of various functional applications and data processing, that is, to achieve the above method.
- the memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.
- the memory 104 may further include memories remotely provided with respect to the processor 102, and these remote memories may be connected to the mobile terminal 10 through a network. Examples of the above network include but are not limited to the Internet, intranet, local area network, mobile communication network, and combinations thereof.
- the transmission device 106 is used to receive or send data via a network.
- the specific example of the network described above may include a wireless network provided by a communication provider of the mobile terminal 10.
- the transmission device 106 includes a network adapter (Network Interface Controller (abbreviated as NIC), which can be connected to other network devices through the base station to communicate with the Internet.
- the transmission device 106 may be a radio frequency (Radio Frequency, RF for short) module, which is used to communicate with the Internet in a wireless manner.
- NIC Network Interface Controller
- FIG. 2 is a flowchart of the information association analysis method according to an embodiment of the present invention. As shown in FIG. 2, the method includes:
- step S201 relevant information of the target object is extracted from the surveillance video, wherein the relevant information of the target object includes: body characteristic information, behavior characteristic information, equipment information;
- Step S203 Multi-dimensional correlation analysis is performed on the extracted relevant information of the target object to obtain a character portrait and behavior track of the target object.
- the relevant information of the target object is extracted from the surveillance video, wherein the relevant information of the target object includes: body characteristic information, behavior characteristic information, equipment information; multi-dimensional correlation analysis is performed on the extracted relevant information of the target object, Get the character portrait and behavior track of the target object.
- the relevant information of the target object includes: body characteristic information, behavior characteristic information, equipment information; multi-dimensional correlation analysis is performed on the extracted relevant information of the target object, Get the character portrait and behavior track of the target object.
- the method before the above step S201, further includes: collecting a surveillance video containing the target object, wherein the surveillance video includes at least one of the following image elements: the target object himself, and The vehicle associated with the target object, the iconic item associated with the target object.
- the above step S201 may be implemented by the following steps: extracting the relevant information of the target object from the surveillance video by using a portrait multi-dimensional model, wherein the portrait multi-dimensional model is a feature that uses multiple dimensions of the human body Vector-based model, portrait multi-dimensional model uses convolutional neural network to extract features from images.
- the above step S201 may be implemented in at least one of the following ways: extracting the body characteristic information of the target object from the surveillance video, wherein the body characteristic information includes at least one of the following: gender, Height, hair accessories, clothing, carry-on items, walking patterns, facial features, head shape, facial ornaments; extract the behavioral feature information of the target object from the surveillance video, where the behavioral feature information includes at least one of the following: transboundary behavior , Active area, wandering area, gathering behavior; extract the target’s equipment information from the surveillance video, where the equipment information includes at least one of the following: vehicle type, license plate, vehicle color, model, brand, car sticker, car Handheld mobile terminal for accessories information and target objects.
- the equipment information includes at least one of the following: vehicle type, license plate, vehicle color, model, brand, car sticker, car Handheld mobile terminal for accessories information and target objects.
- the method before performing multi-dimensional correlation analysis on the extracted relevant information of the target object, the method further includes: acquiring position information of the handheld mobile terminal of the target object according to signal positioning.
- performing multi-dimensional correlation analysis on the extracted relevant information of the target object includes: correlating at least two types of information in the following relevant information of the target object: body characteristic information, Behavioral characteristic information, equipment information, and location information of handheld mobile terminals; comprehensive analysis of the relevant information after association in space-time, region, trajectory, and social relationships.
- the method further includes: predicting the future behavior of the target object based on the result of the comprehensive analysis; and// Or, based on the results of comprehensive analysis, track the location of the target object.
- the method further includes: displaying at least one of the following information on the display screen: character portrait generation information, space-time retrospective display information, path tracking information, behavior habit model, security situation Figure, portrait real-time monitoring information.
- the smart terminal camera collects video information, such as people, vehicles, behaviors, etc., including the precise positioning of the face of the person, facial feature extraction, facial feature comparison, gender, age range, approximate height, hair accessories of the person , A variety of structured description information such as clothing, item carrying, walking form, etc.; various vehicle description information such as vehicle type, license plate, color, model, brand, car stickers, car accessories information; cross-border, area, wandering, aggregation
- Various behavior description information through the multi-dimensional vector analysis model, to mine the depth information of various types of data, combined with the existing police information database, to achieve portrait and path tracking.
- Comprehensively expand the analysis and prediction functions of video data complete the time and space tracing and behavior prediction (crime location, behavior address, etc.) of suspected, suspicious, and suspected persons for multi-dimensional tracking along the entire time axis.
- the multi-dimensional feature model is used for comprehensive analysis, research and characterization of the target face, posture, gait, upper and lower body clothing, head shape, whether to wear glasses, etc.
- the time-for-precision is achieved to describe the target trajectory.
- a single information dimension to the fusion of multi-dimensional information such as people, cars, mobile phones, etc., with "people” as the core object, synchronously discover, identify, generate trajectories for "people", “mobile phones”, “cars”, etc. to achieve trajectory retrieval, Moreover, the trajectories of "people", “car” and “mobile phone” can be merged to form a comprehensive research and judgment, comprehensive analysis, investigation and screening, and characterize the target relationship.
- the inherent, inevitable causal relationship behind the data find the hidden laws, and promote these data from quantitative to qualitative changes, in-depth application and comprehensive application of massive data, with people as the core target resume file, to achieve backward and forward track.
- FIG. 4 is a schematic diagram of multi-dimensional model construction and information research and judgment according to an embodiment of the present invention.
- the multi-dimensional model construction and information research and judgment are divided into four parts: model building, data mining, comprehensive analysis, and fusion research and visualization.
- the establishment of a model is the establishment of a multi-dimensional model of portraits, which comprehensively utilizes multiple dimensional feature vectors such as face, head, clothing, behavior, posture, gait, gender, etc. to establish a multi-dimensional model of portraits, superimposing the time dimension and spatial dimension to improve object recognition Accuracy.
- the convolutional neural network is mainly used to extract the features of the image.
- the convolutional neural network abstracts the high-dimensional features of the image.
- the traditional image algorithm uses artificially set features such as Sift or SURF.
- the artificially set features are often local features, which do not reflect the global features of the image well, and the artificially set feature dimensions are relatively low, which cannot fully reflect the features of the image, and rely heavily on human subjective experience.
- Convolutional neural networks to extract high-dimensional features can extract 512 or 128 or other dimensions of features as needed. Able to synthesize information such as human facial information and clothing. Strong expression skills. Convolutional neural networks include convolutional layers, pooling layers, and fully connected layers.
- the comprehensive analysis is divided into cross-temporal analysis, cross-regional analysis, trajectory blending analysis, cross-time, domain, and trace comprehensive analysis, and social relationship analysis.
- Spatial-temporal analysis Taking "spatial-temporal" as the coordinates, time is used for accuracy. Backtracking the historical spatiotemporal information, obtaining qualitative support, and gradually sampling the existing information, superimposing calculations, can continue to approach precision to achieve the purpose of predicting the future.
- Cross-regional analysis Taking "region” as the coordinate, analyze the concentration of regional crowds, so that the police can dispatch police force distribution in time, control the gathering events, avoid accidents, and take appropriate measures according to the frequency of gathering.
- Trajectory fusion analysis Take the map as the carrier, according to the time and location of the target person, synthesize the map's track route map, find the behavior rules, speculate the target person's behavior purpose, and investigate the clues, so that the police can track and monitor the target person in real time.
- Social relationship analysis use the video surveillance platform to intelligently analyze the video content, extract the fragments of the target personnel, and conduct a unified analysis of the historical fragments of the people nearby to build a social network of the target personnel, so that the police can quickly lock Suspects, investigate individuals or gangs to commit crimes.
- the fusion research and judgment visualization system is divided into character portrait generation, space-time retrospective display, path tracking, behavior habit model, security situation map, and real-time portrait monitoring.
- the object of the present invention is around an individual "person".
- the "person" enters the monitoring area, it can be recognized and identified, and a spatiotemporal file can be established to realize the precise location of the node at a certain time. Monitor their ongoing behavior; by backtracking forward, you can call the online and offline comprehensive behaviors in the past time period to make a pre-judgment on their next behavior; tracking backward, you can continue to observe Its action trajectory provides timely warning and intervention to risks.
- the super intelligent tracking system of the present invention achieves accurate strikes through intelligent tracking of multi-dimensional and multi-domain full time and space.
- multi-dimensional and multi-domain intelligent tracking can be started immediately.
- importing suspect information can obtain accurate behavior trajectory and location information for easy capture; enter the suspect vehicle to obtain accurate parking information Or moving track.
- each video probe, RFID, Wi-Fi probe becomes a 24 hours sleepless policeman, record the phenomenon, and identify the risk of the problem in a timely manner, proactively monitor, track, locate, integrate and analyze the information and data, and comprehensively evaluate , Actively trigger early warning, give decision-making auxiliary suggestions, and the command and dispatch system will promptly issue intervention and disposal instructions to the civilian police.
- the intelligent tracking system of the whole time and space domain of the present invention is essentially a combat system based on supercomputing-based artificial intelligence + big data engine, based on the time axis as a node, with "human" as the core to build a static
- the data and the dynamic data formed by various intelligent perception systems are interlinked, integrated, correlated, comprehensively analyzed and judged to form a comprehensive combat system that can be intelligently discovered, intelligently researched and judged, intelligently triggered, and intelligently tracked.
- the core support it can truly form a transition from passive disposal to active discovery, prediction, prevention and early warning.
- an information correlation analysis device is also provided, which is used to perform the steps in any of the above method embodiments, and the content that has been described will not be repeated here.
- 5 is a structural block diagram of an information correlation analysis device according to an embodiment of the present invention.
- the device includes: an extraction module 50 for extracting related information of a target object from a surveillance video using a portrait multi-dimensional model, Among them, the relevant information of the target object includes: body characteristic information, behavior characteristic information, equipment information; the analysis module 52 is used to perform multi-dimensional correlation analysis on the extracted relevant information of the target object to obtain the character portrait and behavior track of the target object .
- the extraction module 50 uses the portrait multi-dimensional model to extract the relevant information of the target object from the surveillance video, where the relevant information of the target object includes: body characteristic information, behavior characteristic information, equipment information; the analysis module 52 analyzes the extracted target Multi-dimensional correlation analysis is performed on the relevant information of the object to obtain the character portrait and behavior track of the target object. It solves the problem that the manual browsing of surveillance video in the prior art is time-consuming and laborious, and the research and judgment from a single dimension leads to low accuracy.
- the device further includes: an acquisition module for acquiring a surveillance video containing the target object, wherein the surveillance video includes at least one of the following image elements: the target object himself, and The vehicle associated with the target object, the iconic item associated with the target object.
- the extraction module 50 is also used to: use the portrait multi-dimensional model to extract the relevant information of the target object from the surveillance video, wherein the portrait multi-dimensional model is a model built using feature vectors of multiple dimensions of the human body, and the portrait multi-dimensional model uses volume
- the product neural network performs feature extraction on the image.
- the extraction module 50 is also used to: extract the body characteristic information of the target object from the surveillance video, wherein the body characteristic information includes at least one of the following: gender, height, hair accessories, clothing, carry-on items, walking form, facial characteristics, Head shape and facial decorations; extract the behavior characteristic information of the target object from the surveillance video, where the behavior characteristic information includes at least one of the following: cross-border behavior, active area, wandering area, aggregation behavior; extract the target object's behavior from the surveillance video Equipment information, wherein the equipment information includes at least one of the following: vehicle type, license plate, vehicle color, model, brand, car sticker, car accessories information, and target object's handheld mobile terminal.
- the device further includes: a positioning module, configured to locate the acquisition module according to the signal, and used to acquire the position information of the handheld mobile terminal of the target object.
- the analysis module 52 includes: an association unit for correlating at least two types of information in the following related information of the target object: physical characteristic information, behavior characteristic information, equipment information, position information of the handheld mobile terminal; analysis unit, used In order to conduct a comprehensive analysis of the relevant information after the association in time, space, region, trajectory and social relationship.
- the device further includes: a prediction module for predicting the future behavior of the target object based on the result of the comprehensive analysis; and a tracking module for tracking the location of the target object based on the result of the comprehensive analysis.
- the device further includes a display module for displaying at least one of the following information: character portrait generation information, space-time retrospective display information, path tracking information, behavior habit model, safety situation map, and portrait real-time monitoring information.
- the object of the present invention is around an individual "person".
- the "person" enters the monitoring area, it can be recognized and identified, and a spatiotemporal file can be established to realize the precise location of the node at a certain time. Monitor their ongoing behavior; by backtracking forward, you can call the online and offline comprehensive behaviors in the past time period to make a pre-judgment on their next behavior; tracking backward, you can continue to observe Its action trajectory provides timely warning and intervention to risks.
- the super intelligent tracking system of the present invention achieves accurate strikes through intelligent tracking of multi-dimensional and multi-domain full time and space.
- multi-dimensional and multi-domain intelligent tracking can be started immediately.
- importing suspect information can obtain accurate behavior trajectory and location information for easy capture; enter the suspect vehicle to obtain accurate parking information Or moving track.
- each video probe, RFID, Wi-Fi probe becomes a 24 hours sleepless policeman, record the phenomenon, and identify the risk of the problem in a timely manner, proactively monitor, track, locate, integrate and analyze the information and data, and comprehensively evaluate , Actively trigger early warning, give decision-making auxiliary suggestions, and the command and dispatch system will promptly issue intervention and disposal instructions to the civilian police.
- the intelligent tracking system of the whole time and space domain of the present invention is essentially a combat system based on supercomputing-based artificial intelligence + big data engine, based on the time axis as a node, with "human" as the core to build a static
- the data and the dynamic data formed by various intelligent perception systems are interlinked, integrated, correlated, comprehensively analyzed and judged to form a comprehensive combat system that can be intelligently discovered, intelligently researched and judged, intelligently triggered, and intelligently tracked.
- the core support it can truly form a transition from passive disposal to active discovery, prediction, prevention and early warning.
- An embodiment of the present invention further provides a storage medium in which a computer program is stored, wherein the computer program is set to execute any of the steps in the above method embodiments during runtime.
- the above storage medium may be set to store a computer program for performing the following steps:
- the storage medium is also configured to store a computer program for performing the following steps:
- Collect a surveillance video containing the target object wherein the surveillance video includes at least one of the following image elements: the target object himself, the vehicle associated with the target object, and the target object Iconic items.
- the storage medium is also configured to store a computer program for performing the following steps:
- a portrait multi-dimensional model is used to extract relevant information of the target object from the surveillance video, wherein the portrait multi-dimensional model is a model built using feature vectors of multiple dimensions of the human body, and the portrait multi-dimensional model uses a convolutional neural network to characterize the image extract.
- the foregoing storage medium may include, but is not limited to: a U disk, a read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory (referred to as RAM), mobile hard disk, magnetic disk or optical disk and other media that can store computer programs.
- ROM read-only memory
- RAM random access memory
- mobile hard disk magnetic disk or optical disk and other media that can store computer programs.
- An embodiment of the present invention further provides an electronic device, including a memory and a processor, where the computer program is stored in the memory, and the processor is configured to run the computer program to perform any of the steps in the above method embodiments.
- the electronic device may further include a transmission device and an input-output device, wherein the transmission device is connected to the processor, and the input-output device is connected to the processor.
- the foregoing processor may be configured to perform the following steps through a computer program:
- the processor is also configured to store a computer program for performing the following steps:
- Collect a surveillance video containing the target object wherein the surveillance video includes at least one of the following image elements: the target object himself, the vehicle associated with the target object, and the target object Iconic items.
- the processor is also configured to store a computer program for performing the following steps:
- the portrait multi-dimensional model uses a convolutional neural network to characterize the image extract.
- modules or steps of the present invention can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed in a network composed of multiple computing devices Above, optionally, they can be implemented with program code executable by the computing device, so that they can be stored in the storage device to be executed by the computing device, and in some cases, can be in a different order than here
- the steps shown or described are performed, or they are made into individual integrated circuit modules respectively, or multiple modules or steps among them are made into a single integrated circuit module for implementation. In this way, the present invention is not limited to any specific combination of hardware and software.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- General Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Social Psychology (AREA)
- Psychiatry (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Image Analysis (AREA)
Abstract
Description
Claims (11)
- 一种信息的关联分析方法,其特征在于,包括:An information association analysis method, which is characterized by including:从监控视频中提取目标对象的相关信息,其中,所述目标对象的相关信息包括:身体特征信息、行为特征信息、装备信息;Extract the relevant information of the target object from the surveillance video, wherein the relevant information of the target object includes: body characteristic information, behavior characteristic information, equipment information;对提取的所述目标对象的相关信息进行多维度的关联分析,得到所述目标对象的人物画像和行为轨迹。Multi-dimensional correlation analysis is performed on the extracted relevant information of the target object to obtain a character portrait and behavior track of the target object.
- 根据权利要求1所述的方法,其特征在于,从监控视频中提取目标对象的相关信息之前,所述方法还包括:The method according to claim 1, wherein before extracting the relevant information of the target object from the surveillance video, the method further comprises:采集包含所述目标对象的监控视频,其中,所述监控视频中至少包括以下之一的图像元素:所述目标对象本人,与所述目标对象相关联的交通工具,与所述目标对象相关联的标志性物品。Collect a surveillance video containing the target object, wherein the surveillance video includes at least one of the following image elements: the target object himself, the vehicle associated with the target object, and the target object Iconic items.
- 根据权利要求1所述的方法,其特征在于,从监控视频中提取目标对象的相关信息包括:The method according to claim 1, wherein extracting the relevant information of the target object from the surveillance video includes:利用人像多维模型从监控视频中提取目标对象的相关信息,其中,所述人像多维模型为利用人体的多个维度的特征矢量建立的模型,所述人像多维模型利用卷积神经网络对图像进行特征提取。Use the portrait multi-dimensional model to extract the relevant information of the target object from the surveillance video, wherein the portrait multi-dimensional model is a model built using feature vectors of multiple dimensions of the human body, and the portrait multi-dimensional model uses a convolutional neural network to characterize the image extract.
- 根据权利要求1所述的方法,其特征在于,从监控视频中提取目标对象的相关信息,包括:The method according to claim 1, wherein the relevant information of the target object extracted from the surveillance video includes:从监控视频中提取目标对象的所述身体特征信息,其中,所述身体特征信息至少包括以下之一:性别、身高、发饰、衣着、随身携带的物品、步履形态、面部特征、头型、面部装饰品;Extract the body characteristic information of the target object from the surveillance video, wherein the body characteristic information includes at least one of the following: gender, height, hair accessories, clothing, carry-on items, walking shape, facial characteristics, head shape, Facial decorations从监控视频中提取目标对象的所述行为特征信息,其中,所述行为特征信息至少包括以下之一:越界行为、活动区域、徘徊区域、聚集行为;Extracting the behavior characteristic information of the target object from the surveillance video, wherein the behavior characteristic information includes at least one of the following: cross-border behavior, active area, wandering area, aggregation behavior;从监控视频中提取目标对象的所述装备信息,其中,所述装备信息至少包括以下之一:交通工具类别、牌照、交通工具的颜色、车型、品牌、车贴、车饰物信息、所述目标对象的手持移动终端。Extract the equipment information of the target object from the surveillance video, where the equipment information includes at least one of the following: vehicle type, license plate, vehicle color, model, brand, car sticker, car accessories information, the target Target's handheld mobile terminal.
- 根据权利要求4所述的方法,其特征在于,对提取的所述目标对象的相关信息进行多维度的关联分析之前,所述方法包括:The method according to claim 4, wherein before performing multi-dimensional correlation analysis on the extracted relevant information of the target object, the method comprises:根据信号定位获取所述目标对象的手持移动终端的位置信息。Acquire the position information of the handheld mobile terminal of the target object according to the signal positioning.
- 根据权利要求5所述的方法,其特征在于,对提取的所述目标对象的相关信息进行多维度的关联分析包括:The method according to claim 5, wherein performing multi-dimensional correlation analysis on the extracted relevant information of the target object includes:将所述目标对象的以下相关信息中的至少两类信息进行关联:所述身体特征信息、所述行为特征信息、所述装备信息、所述手持移动终端的位置信息;Associate at least two types of information in the following related information of the target object: the physical characteristic information, the behavior characteristic information, the equipment information, and the position information of the handheld mobile terminal;对关联后的所述相关信息进行时空、地域、轨迹和社会关系的综合分析。Comprehensive analysis of the relevant information after the association in time, space, region, trajectory and social relationship.
- 根据权利要求6所述的方法,其特征在于,对关联后的所述相关信息进行时空、地域、轨迹的综合分析之后,所述方法还包括:The method according to claim 6, characterized in that, after a comprehensive analysis of the related information after the association in time, space, region and trajectory, the method further comprises:根据所述综合分析的结果,预测所述目标对象的未来行为;和/或Predict the future behavior of the target object based on the results of the comprehensive analysis; and/or根据所述综合分析的结果,追踪所述目标对象的位置。According to the result of the comprehensive analysis, track the position of the target object.
- 根据权利要求7中所述的方法,其特征在于,所述方法还包括:The method according to claim 7, wherein the method further comprises:在显示屏上显示以下至少之一的信息:人物画像生成信息、时空回溯展示信息、路径追踪信息、行为习惯模型、安全态势图、人像实时监控信息。At least one of the following information is displayed on the display screen: character portrait generation information, space-time retrospective display information, path tracking information, behavior habit model, security situation map, and real-time portrait monitoring information.
- 一种信息的关联分析装置,其特征在于,包括:An information correlation analysis device, characterized in that it includes:提取模块,用于利用人像多维模型,从监控视频中提取目标对象的相关信息,其中,所述目标对象的相关信息包括:身体特征信息、行为特征信息、装备信息;The extraction module is used to extract the relevant information of the target object from the surveillance video by using the multi-dimensional portrait model, wherein the relevant information of the target object includes: body characteristic information, behavior characteristic information, equipment information;分析模块,用于对提取的所述目标对象的相关信息进行多维度的关联分析,得到所述目标对象的人物画像和行为轨迹。The analysis module is configured to perform multi-dimensional correlation analysis on the extracted relevant information of the target object to obtain a character portrait and behavior track of the target object.
- 一种存储介质,其特征在于,所述存储介质中存储有计算机程序,其中,所述计算机程序被设置为运行时执行所述权利要求1至8任一项中所述的方法。A storage medium, characterized in that a computer program is stored in the storage medium, wherein the computer program is configured to execute the method described in any one of claims 1 to 8 during runtime.
- 一种电子装置,包括存储器和处理器,其特征在于,所述存储器中存储有计算机程序,所述处理器被设置为运行所述计算机程序以执行所述权利要求1至8任一项中所述的方法。An electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of claims 1 to 8. Described method.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811485809.2 | 2018-12-06 | ||
CN201811485809.2A CN111291589B (en) | 2018-12-06 | Information association analysis method and device, storage medium and electronic device |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2020114138A1 true WO2020114138A1 (en) | 2020-06-11 |
Family
ID=70974096
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2019/112925 WO2020114138A1 (en) | 2018-12-06 | 2019-10-24 | Information associated analysis method and apparatus, and storage medium and electronic device |
Country Status (1)
Country | Link |
---|---|
WO (1) | WO2020114138A1 (en) |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111950937A (en) * | 2020-09-01 | 2020-11-17 | 上海海事大学 | Key personnel risk assessment method based on fusion space-time trajectory |
CN112218046A (en) * | 2020-09-27 | 2021-01-12 | 杭州海康威视系统技术有限公司 | Object monitoring method and device |
CN112388678A (en) * | 2020-11-04 | 2021-02-23 | 公安部第三研究所 | Behavior detection robot based on low-power-consumption pattern recognition technology |
CN113628479A (en) * | 2021-08-16 | 2021-11-09 | 成都民航空管科技发展有限公司 | Video-based tower control information fusion system and method |
CN113641660A (en) * | 2021-08-31 | 2021-11-12 | 联通(广东)产业互联网有限公司 | Big data analysis system for retired soldier |
CN113793174A (en) * | 2021-09-01 | 2021-12-14 | 北京爱笔科技有限公司 | Data association method and device, computer equipment and storage medium |
CN115345907A (en) * | 2022-10-18 | 2022-11-15 | 广东电网有限责任公司中山供电局 | Target dynamic tracking device based on edge calculation |
CN115457449A (en) * | 2022-11-11 | 2022-12-09 | 深圳市马博士网络科技有限公司 | Early warning system based on AI video analysis and monitoring security protection |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104599443A (en) * | 2015-01-12 | 2015-05-06 | 江苏省交通规划设计院股份有限公司 | Vehicle-mounted forewarning terminal for driving behaviors based on information fusion and forewarning method thereof |
CN104866860A (en) * | 2015-03-20 | 2015-08-26 | 武汉工程大学 | Indoor human body behavior recognition method |
CN105160313A (en) * | 2014-09-15 | 2015-12-16 | 中国科学院重庆绿色智能技术研究院 | Method and apparatus for crowd behavior analysis in video monitoring |
US20170367651A1 (en) * | 2016-06-27 | 2017-12-28 | Facense Ltd. | Wearable respiration measurements system |
-
2019
- 2019-10-24 WO PCT/CN2019/112925 patent/WO2020114138A1/en active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105160313A (en) * | 2014-09-15 | 2015-12-16 | 中国科学院重庆绿色智能技术研究院 | Method and apparatus for crowd behavior analysis in video monitoring |
CN104599443A (en) * | 2015-01-12 | 2015-05-06 | 江苏省交通规划设计院股份有限公司 | Vehicle-mounted forewarning terminal for driving behaviors based on information fusion and forewarning method thereof |
CN104866860A (en) * | 2015-03-20 | 2015-08-26 | 武汉工程大学 | Indoor human body behavior recognition method |
US20170367651A1 (en) * | 2016-06-27 | 2017-12-28 | Facense Ltd. | Wearable respiration measurements system |
Cited By (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111950937A (en) * | 2020-09-01 | 2020-11-17 | 上海海事大学 | Key personnel risk assessment method based on fusion space-time trajectory |
CN111950937B (en) * | 2020-09-01 | 2023-12-01 | 上海海事大学 | Important personnel risk assessment method based on fusion of space-time trajectories |
CN112218046A (en) * | 2020-09-27 | 2021-01-12 | 杭州海康威视系统技术有限公司 | Object monitoring method and device |
CN112218046B (en) * | 2020-09-27 | 2023-10-24 | 杭州海康威视系统技术有限公司 | Object monitoring method and device |
CN112388678A (en) * | 2020-11-04 | 2021-02-23 | 公安部第三研究所 | Behavior detection robot based on low-power-consumption pattern recognition technology |
CN113628479A (en) * | 2021-08-16 | 2021-11-09 | 成都民航空管科技发展有限公司 | Video-based tower control information fusion system and method |
CN113641660A (en) * | 2021-08-31 | 2021-11-12 | 联通(广东)产业互联网有限公司 | Big data analysis system for retired soldier |
CN113793174A (en) * | 2021-09-01 | 2021-12-14 | 北京爱笔科技有限公司 | Data association method and device, computer equipment and storage medium |
CN115345907A (en) * | 2022-10-18 | 2022-11-15 | 广东电网有限责任公司中山供电局 | Target dynamic tracking device based on edge calculation |
CN115345907B (en) * | 2022-10-18 | 2022-12-30 | 广东电网有限责任公司中山供电局 | Target dynamic tracking device based on edge calculation |
CN115457449A (en) * | 2022-11-11 | 2022-12-09 | 深圳市马博士网络科技有限公司 | Early warning system based on AI video analysis and monitoring security protection |
Also Published As
Publication number | Publication date |
---|---|
CN111291589A (en) | 2020-06-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2020114138A1 (en) | Information associated analysis method and apparatus, and storage medium and electronic device | |
Yang et al. | Visual perception enabled industry intelligence: state of the art, challenges and prospects | |
Fan et al. | Learning longterm representations for person re-identification using radio signals | |
CN109934176B (en) | Pedestrian recognition system, recognition method, and computer-readable storage medium | |
CN109271554B (en) | Intelligent video identification system and application thereof | |
Franklin et al. | Anomaly detection in videos for video surveillance applications using neural networks | |
Han et al. | Deep learning-based workers safety helmet wearing detection on construction sites using multi-scale features | |
CN109543312B (en) | Space-time investigation analysis method and system | |
US20210382933A1 (en) | Method and device for archive application, and storage medium | |
CN111860430B (en) | Identification method and device of fighting behavior, storage medium and electronic device | |
CN106355154B (en) | Method for detecting frequent passing of people in surveillance video | |
JP6807925B2 (en) | Video identification and analysis recognition system | |
CN110472532B (en) | Video object behavior identification method and device | |
CN110659391A (en) | Video detection method and device | |
Küçükkeçeci et al. | Multilevel object tracking in wireless multimedia sensor networks for surveillance applications using graph-based big data | |
WO2020103293A1 (en) | Method, device, and electronic device for presenting individual search information | |
CN114550053A (en) | Traffic accident responsibility determination method, device, computer equipment and storage medium | |
CN109670517A (en) | Object detection method, device, electronic equipment and target detection model | |
Shahabi et al. | Janus-multi source event detection and collection system for effective surveillance of criminal activity | |
CN110119730A (en) | A kind of monitor video processing method, system, terminal and storage medium | |
Ntouskos et al. | Saliency prediction in the coherence theory of attention | |
CN111291589B (en) | Information association analysis method and device, storage medium and electronic device | |
de Campos | A survey on computer vision tools for action recognition, crowd surveillance and suspect retrieval | |
Xu et al. | Violent Physical Behavior Detection using 3D Spatio-Temporal Convolutional Neural Networks | |
Vigil et al. | EYE SPY face detection and identification using YOLO |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 19892263 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19892263 Country of ref document: EP Kind code of ref document: A1 |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19892263 Country of ref document: EP Kind code of ref document: A1 |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205 DATED 29.04.2022) |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 19892263 Country of ref document: EP Kind code of ref document: A1 |