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WO2018121127A1 - Système de recueil de statistiques sur un trafic piéton au moyen d'un suivi basé sur une technique d'analyse de vidéo - Google Patents

Système de recueil de statistiques sur un trafic piéton au moyen d'un suivi basé sur une technique d'analyse de vidéo Download PDF

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Publication number
WO2018121127A1
WO2018121127A1 PCT/CN2017/111929 CN2017111929W WO2018121127A1 WO 2018121127 A1 WO2018121127 A1 WO 2018121127A1 CN 2017111929 W CN2017111929 W CN 2017111929W WO 2018121127 A1 WO2018121127 A1 WO 2018121127A1
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WIPO (PCT)
Prior art keywords
module
human body
trajectory
contour
recognition model
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PCT/CN2017/111929
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English (en)
Chinese (zh)
Inventor
周圣强
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苏州万店掌网络科技有限公司
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Publication of WO2018121127A1 publication Critical patent/WO2018121127A1/fr

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V20/53—Recognition of crowd images, e.g. recognition of crowd congestion

Definitions

  • the present invention relates to the field of video analysis technologies, and in particular, to a system for tracking statistic traffic based on video analysis technology.
  • the traditional human traffic statistics are two kinds of infrared counters and mechanical counters.
  • the infrared counters use infrared sensors to sense the statistics of the personnel, but when there are many people passing by, some people are difficult to be sensed by the infrared sensor because of the occlusion. And the infrared counter intelligently recognizes that someone enters and exits, cannot determine the direction, is prone to false positives, and has low accuracy.
  • the mechanical counter needs to embed the induction pedal in the passage port, and the counter is used to count the personnel counting. This method is inaccurate when the flow rate is large, and it is only a rough statistical number of people passing through. The person is entering or going out.
  • the feature points of some motions are tracked first, then the trajectories of the feature points are clustered and analyzed, so that the flow information is obtained.
  • the feature points themselves are difficult to track stably, and the technical precision is poor.
  • the second is based on the human body segmentation tracking method, first extracting the moving target block, then segmenting the moving target block to obtain a single human target, and finally tracking each human target to achieve human flow statistics. If the human body is in the occlusion state, the accuracy of human body segmentation is difficult to be guaranteed, which affects statistical accuracy.
  • the third method is based on the detection and tracking of the head or the head and shoulder.
  • the method detects the head or the head and shoulders in the video, and performs the flow statistics by tracking the head or the head and shoulders. This method can only detect the same type of target, and cannot detect different types of targets at the same time.
  • the object of the present invention is to overcome the above problems existing in the prior art, and to provide a system for tracking statistic person traffic based on a video analysis mode.
  • the system of the present invention can not only accurately identify people entering and exiting, but also can determine direction and false positive rate. Lower.
  • a system for tracking statistic traffic based on video analytics technology comprising:
  • the storage module stores a pre-established human body recognition model, and the storage module further stores a motion track preset in advance;
  • the comparison module compares image information acquired by the camera with a human body recognition model stored in the storage module;
  • a positioning module after the comparison module performs the comparison of the human body recognition module, if the acquired image information is successfully compared with the human body recognition model, the first step is performed;
  • trajectory forming module tracks the recognized human body image information based on the first step of the positioning module to form a human body moving trajectory in a three-dimensional space
  • the direction module compares a human body moving track formed by the track forming module with a preset motion track in the storage module, and determines a direction of a human body moving track;
  • the counting module counts the flow of the human body according to the direction of the human body moving track determined by the direction module;
  • the calculation module performs statistical calculation on the incoming traffic and the output traffic calculated by the counting module, and then analyzes with the background system data, and performs calculation according to the sales amount and the sales order number to obtain the conversion rate of the human flow.
  • the method further includes an extraction module, the extraction module extracts a human body contour from the image information acquired by the camera, and transmits the contour to the comparison module, where the comparison module extracts the human body contour extracted by the extraction module Compare with the stored human body recognition model.
  • the comparison module further comprises, when comparing the extracted human body contour with the stored human body recognition module, a threshold analysis of the human body contour contrast similarity, wherein the similarity between the human body contour and the stored human body recognition model is higher than a threshold value.
  • a threshold analysis of the human body contour contrast similarity wherein the similarity between the human body contour and the stored human body recognition model is higher than a threshold value.
  • the trajectory forming module defines an entering direction as X, and an outgoing direction as Y, and after the first positioning by the positioning module, respectively reduces or increases the trajectory in two directions of X and Y. , to calculate the movement trajectory.
  • the trajectory forming module further performs smoothness analysis on the human body moving trajectory, determines whether the smoothness satisfies the threshold of the pre-custom trajectory, and if so, retains the human body moving trajectory, and if not, quits the human body moving trajectory.
  • the scene dividing module further performs scene division on the detection area in the image, and obtains a variation range of the size of the human body image in the detection area by scene division.
  • the calculation module forms a report for the statistics of the flow of the person, and analyzes the operation status of the store according to the report.
  • the system of the invention first establishes a human body model in advance, and then compares the comparison module, first determines the acquired image as a person, reduces the false positive rate of the system; first customizes a preset trajectory, obtains a moving trajectory in a three-dimensional space, and compares the trajectory according to the trajectory. To determine the direction of people entering and exiting, it is possible to accurately identify the entry and exit of people, and also to determine the direction with high precision.
  • the system of the invention can automatically generate reports, obtain data such as passenger flow, customer volume, number of stops, etc., and can comprehensively analyze data such as sales and sales orders in the background, obtain data information such as passenger flow conversion rate, and analyze the merchants.
  • the store situation is of great significance.
  • Figure 1 is a schematic diagram of the system of the present invention.
  • the hardware mainly includes a camera, a storage device, a CPU computing device, and the like.
  • the functions include: a storage module, an extraction module, a comparison module, a positioning module, a track formation module, a direction module, a counting module, a calculation module, and a scene division module.
  • the camera is used to obtain image information, and the camera setting position is set according to a specific scene and stored in the storage module.
  • a pre-established human body recognition model and storing a motion trajectory of the pre-customized setting in the storage module;
  • the comparison module compares the image information acquired by the camera with the human body recognition model stored in the storage module; before the comparison, the human body contour is first performed
  • the extraction and extraction module extracts the human body contour from the image information acquired by the camera, and transmits the contour to the comparison module, and the comparison module compares the human body contour extracted by the extraction module with the stored human body recognition model.
  • the contrast module compares the extracted human body contour with the stored human body recognition module, and also includes a threshold analysis of the human body contour contrast similarity.
  • the human body contour and the stored human body recognition model have a similarity similar to the threshold value
  • the human body contour and the human body contour When the recognition model is successfully matched, when the similarity between the human body contour and the stored human body recognition module is lower than the threshold value, the human body contour fails to match the human body recognition model, and the image acquired by the camera cannot be defined as a person.
  • the trajectory forming module tracks the human body image information after the recognition based on the first step of the positioning module to form The movement of the human body in three-dimensional space.
  • the trajectory forming module defines the entering direction as X and the outgoing direction as Y. After the first positioning by the positioning module, the movement is calculated by reducing or increasing the trajectory in the X and Y directions respectively. Track.
  • the trajectory forming module also performs smoothness analysis on the trajectory of the human body to determine whether the smoothness satisfies the threshold of the pre-custom trajectory. If it is satisfied, the trajectory of the human body is retained, and if not, the trajectory of the human body is discarded.
  • the direction module compares the movement track of the human body formed by the track forming module with the preset motion track in the storage module to determine the direction of the human body moving track; then the counting module counts the human flow according to the direction of the human body moving track determined by the direction module; The module counts the incoming traffic and the output traffic for statistical calculation, and then analyzes with the background system data, and calculates according to the sales amount and the sales order number to obtain the conversion rate of the person flow.
  • the calculation module forms a report for the statistics of the flow of people, and analyzes the operation status of the store according to the report.
  • the scene dividing module performs scene division on the detection area in the image, and obtains a variation range of the size of the human body image in the detection area by scene division.
  • the camera acquires the human body image in the scene division module, and then extracts the human body contour
  • the extraction module extracts the human body contour from the image information acquired by the camera, and transmits
  • the comparison module compares the human body contour extracted by the extraction module with the stored human body recognition model.
  • the comparison module performs the human body recognition module comparison, if the acquired image information and the human body recognition model Contrast success, first step positioning; track formation The module is based on the first step of the positioning module to track the recognized human body image information to form a human body moving trajectory in a three-dimensional space; perform smoothness analysis on the human body moving trajectory to determine whether the smoothness satisfies the threshold of the pre-customized trajectory, If satisfied, the human body movement track is retained, and if not satisfied, the human body moving track is discarded; the direction module compares the human body moving track formed by the track forming module with the preset motion track in the storage module to determine the direction

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un système destiné à recueillir des statistiques sur un trafic piéton au moyen d'un suivi basé sur une technique d'analyse de vidéo. Le système comporte: une caméra, un module de stockage, un module de comparaison, un module de positionnement, un module de formation de trajectoires, un module directionnel, un module de comptage, un module de calcul, un modèle préconstruit de reconnaissance de corps et une trajectoire de mouvement, le module de comparaison comparant des informations d'image obtenues par la caméra avec un modèle stocké de reconnaissance de corps, et si la comparaison réussit, un positionnement de première étape étant réalisé; le module de formation de trajectoires suivant, d'après le positionnement de première étape, des informations d'image de corps reconnu pour former une trajectoire de mouvement de corps dans un espace tridimensionnel; le module directionnel comparant la trajectoire de mouvement de corps à une trajectoire de mouvement prédéfinie dans le module de stockage pour déterminer la direction de la trajectoire de mouvement de corps; et le module de comptage recueillant des statistiques sur le trafic piéton d'après la direction de la trajectoire de mouvement de corps. Le système selon la présente invention peut non seulement reconnaître avec exactitude la présence de personnes entrant et sortant, mais également déterminer la direction, d'où un taux de faux positifs relativement bas.
PCT/CN2017/111929 2016-12-30 2017-11-20 Système de recueil de statistiques sur un trafic piéton au moyen d'un suivi basé sur une technique d'analyse de vidéo WO2018121127A1 (fr)

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CN113051975A (zh) * 2019-12-27 2021-06-29 深圳云天励飞技术有限公司 人流量统计方法及相关产品
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CN117226843A (zh) * 2023-09-27 2023-12-15 盐城工学院 一种基于视觉伺服的机器人移动轨迹控制方法及系统
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CN109583411A (zh) * 2018-12-09 2019-04-05 大连海事大学 基于tof摄像头的游客类别在线审核方法
CN110175562A (zh) * 2019-05-24 2019-08-27 深圳职业技术学院 一种基于并行计算的gpu加速行人检测算法
CN110175562B (zh) * 2019-05-24 2024-01-16 深圳职业技术大学 一种基于并行计算的gpu加速行人检测算法
CN113051975A (zh) * 2019-12-27 2021-06-29 深圳云天励飞技术有限公司 人流量统计方法及相关产品
CN113051975B (zh) * 2019-12-27 2024-04-02 深圳云天励飞技术有限公司 人流量统计方法及相关产品
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CN111860261A (zh) * 2020-07-10 2020-10-30 北京猎户星空科技有限公司 一种客流值的统计方法、装置、设备及介质
CN111914754A (zh) * 2020-08-03 2020-11-10 杭州云栖智慧视通科技有限公司 一种图像内容相似度度量方法、装置及计算机设备
CN114529859A (zh) * 2020-10-30 2022-05-24 广东飞企互联科技股份有限公司 一种人员流量统计方法和统计系统
CN113326830A (zh) * 2021-08-04 2021-08-31 北京文安智能技术股份有限公司 基于俯视图像的客流统计模型训练方法和客流统计方法
CN115215177A (zh) * 2022-08-26 2022-10-21 中建八局发展建设有限公司 一种电梯升降智能识别系统和方法
CN116542858B (zh) * 2023-07-03 2023-09-05 众芯汉创(江苏)科技有限公司 一种基于空间轨迹的数据拼接分析系统
CN116542858A (zh) * 2023-07-03 2023-08-04 众芯汉创(江苏)科技有限公司 一种基于空间轨迹的数据拼接分析系统
CN117226843A (zh) * 2023-09-27 2023-12-15 盐城工学院 一种基于视觉伺服的机器人移动轨迹控制方法及系统
CN117226843B (zh) * 2023-09-27 2024-02-27 盐城工学院 一种基于视觉伺服的机器人移动轨迹控制方法及系统
CN117854154A (zh) * 2024-01-17 2024-04-09 内蒙古工业大学 基于计算机视觉的动物识别跟踪与行为检测系统
CN119090059A (zh) * 2024-08-14 2024-12-06 国网江苏省电力有限公司扬州供电分公司 一种基于大数据的区域能源监控系统

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