[go: up one dir, main page]

WO2023273011A1 - Procédé, appareil et dispositif de détection d'un objet lancé de haut et support de stockage informatique - Google Patents

Procédé, appareil et dispositif de détection d'un objet lancé de haut et support de stockage informatique Download PDF

Info

Publication number
WO2023273011A1
WO2023273011A1 PCT/CN2021/123512 CN2021123512W WO2023273011A1 WO 2023273011 A1 WO2023273011 A1 WO 2023273011A1 CN 2021123512 W CN2021123512 W CN 2021123512W WO 2023273011 A1 WO2023273011 A1 WO 2023273011A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
optical flow
measured
parabolic
center point
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/CN2021/123512
Other languages
English (en)
Chinese (zh)
Other versions
WO2023273011A9 (fr
Inventor
蔡官熊
赵晨
方宝乐
曾星宇
赵瑞
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Sensetime Technology Co Ltd
Original Assignee
Shenzhen Sensetime Technology Co Ltd
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 Shenzhen Sensetime Technology Co Ltd filed Critical Shenzhen Sensetime Technology Co Ltd
Publication of WO2023273011A1 publication Critical patent/WO2023273011A1/fr
Publication of WO2023273011A9 publication Critical patent/WO2023273011A9/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/269—Analysis of motion using gradient-based methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30232—Surveillance
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30241—Trajectory

Definitions

  • the high-altitude parabolic detection equipment can sequentially read two adjacent images to be tested from multiple frames of images to be tested according to the time sequence of image acquisition, and generate adjacent images through the preset optical flow model.
  • the position coordinates of the center point of the dropped object in the photometric flow image determine the motion track of the dropped object; and the high-altitude parabolic detection process is performed based on the motion track.
  • FIG. 1 is a schematic diagram of the implementation flow of the high-altitude parabolic detection method proposed by the embodiment of the present disclosure
  • FIG. 8 is a schematic diagram of the implementation flow of the high-altitude parabolic detection method proposed by the embodiment of the present disclosure VII;
  • the frame-to-frame difference method can obtain the outer contour of the moving target, the method is simple and the computational complexity is small, but it has the defects of fixed camera, robust phase difference and low precision, and is generally only suitable for simple real-time motion detection.
  • the basic principle of the background subtraction method is to subtract the current frame in the image sequence from the background reference model (background image) that has been determined or acquired in real time, find the difference, and calculate the area where the pixel difference with the background image exceeds a certain threshold. It is the motion area, so as to determine the motion position, outline, size and other characteristics.
  • FIG. 1 is a schematic diagram of the implementation flow of the high-altitude parabolic detection method proposed by the embodiment of the present disclosure.
  • the high-altitude parabolic The detection method may include the following steps:
  • the image to be tested may be an RGB color image, a grayscale image, or other sensor image data (such as an infrared image).
  • the range of images it can acquire may include target monitoring objects (such as buildings) and greenery or fixed equipment downstairs, such as light poles and trees.
  • target monitoring objects such as buildings
  • greenery or fixed equipment downstairs such as light poles and trees.
  • green facilities such as trees will have a certain impact on the detection results of moving objects. The impact can be that fallen leaves are easily judged as high-altitude objects, and additional processing tasks are brought to image post-processing, slowing down the high-altitude Speed of parabola detection processing.
  • the initial image refers to an image within a monitoring range that is collected in real time by an image acquisition monitoring device, or a video within a monitoring range that is locally historically stored.
  • a polygonal contour C that is, a preset polygonal contour
  • the region of interest R is demarcated from the initial image by the preset polygonal contour as the target detection region, ignoring the Other interfering graphic content.
  • the high-altitude parabolic detection device can perform binarization processing on the initial image based on the preset polygonal outline, first set the image pixels of the target detection area corresponding to the preset polygonal outline to 1, and set the image pixels outside the target detection area to 0, Then perform pixel point multiplication between the binarized image and the original image, so that the image of the target detection area retains the original pixel value, and the image pixels outside this area are all 0, so as to determine the target detection area existing in the initial image .
  • image segmentation processing may be performed on the initial image based on the minimum detection frame, that is, image frame cropping, and then the image to be tested may be obtained.
  • S110 Determine, from the optical flow images, the optical flow images to be measured that contain the dropped objects, and determine the position coordinates of the center points of the dropped objects in each optical flow image to be measured.
  • the obtained optical flow images can be further analyzed and processed, and the current optical flow images determined to be excessively noisy, with too many types of moving objects or abnormal optical flow images can be filtered, and the current optical flow images can be filtered out.
  • FIG. 3 is a schematic diagram of a motion trajectory of a dropped object proposed by an embodiment of the present disclosure, and the motion trajectory is formed by position coordinates of multiple center points of the dropped object in multiple consecutive frames of optical flow images to be measured.
  • a single-frame binarization method can be used to determine whether there is a real falling object in the current optical flow image Judgment is made to determine the optical flow image to be measured where there is a thrown object.
  • the region where the pixel value in the image is greater than or equal to the critical pixel gray value as the region where the moving object exists, and the The pixel value of the area in the binary image is set as the first pixel value, otherwise, the area where the pixel value in the image is smaller than the critical pixel gray value is determined as the area where the non-moving object is located, and the pixel value of the area in the binary image If the value is set to the second pixel value, the binary image corresponding to any optical flow image can be obtained.
  • the grayscale image conversion can be performed based on the following formula:
  • the mean value calculation can be performed based on the coordinate subsets of each type of moving objects, and the determined coordinate mean value can be used as each The center point position coordinates of a class of moving objects, and from the normalized grayscale image corresponding to the optical flow image to be measured, determine the pixel gray value at the corresponding position based on the center point position coordinates.
  • the user can view the parabolic event interval, such as a surveillance video corresponding to the parabolic event, although the optical flow to be measured may be taken when there may be multiple thrown objects
  • the position coordinates of the center point of the moving object with the largest optical flow amplitude in the image are used as the position coordinates of the parabolic object, but after the parabolic event interval is determined, you can view the event interval corresponding to each falling object that may exist in the interval Parabolic events to view.
  • the parabolic event is not a real high-altitude parabolic event, it may be similar to a person passing an object by hand, or lifting an object above his head, such as an event where a person stands at the window and eats an apple, and the hand picks up the apple Put it on your mouth, bite your hand and put it down. Therefore, the angle between the fitted line and the vertical direction can be calculated, which can be used as one of the factors for judging whether a parabolic event is a high-altitude parabolic event.
  • the pixel value at the coordinates of the point position, and these pixel values are cumulatively summed to obtain the pixel cumulative value, and the pixel cumulative value is used as one of the factors for judging whether the parabolic event is a high-altitude parabolic event.
  • the pixel accumulation value is less than a preset pixel threshold, and the coordinate difference is greater than a preset height threshold, determine that the dropped object is a high-altitude thrown object, And the corresponding parabolic event is a high-altitude parabolic event.
  • the dense optical flow calculation specifically includes the following steps:
  • S206 Perform binarization processing on the normalized grayscale image to obtain a binary image corresponding to the optical flow image; wherein, the binary image includes a foreground moving object with a first pixel value and a background non-moving object with a second pixel value .
  • FIG. 12 is a schematic diagram of the composition and structure of the high-altitude parabolic detection device proposed by the embodiment of the present disclosure.
  • the high-altitude parabolic detection device 10 includes an acquisition part 11 , a generation part 12 , a determination part 13 , and a processing part 14 .
  • the generating part 12 is configured to generate optical flow images corresponding to the two adjacent images to be tested through a preset optical flow model
  • the processing part 14 is configured to perform high-altitude parabolic detection processing based on the motion trajectory.
  • the memory 22 is used to store instructions and data.
  • the program instructions corresponding to a high-altitude parabolic detection method in this embodiment can be stored on a storage medium such as an optical disc, a hard disk, and a USB flash drive.
  • a storage medium such as an optical disc, a hard disk, and a USB flash drive.
  • These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable parabolic detection device to operate in a specific manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising instruction means, the The instruction means implements the functions specified in implementing one or more procedures of the flowchart and/or one or more blocks of the block diagram.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Data Mining & Analysis (AREA)
  • General Physics & Mathematics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Multimedia (AREA)
  • Geometry (AREA)
  • Image Analysis (AREA)

Abstract

Des modes de réalisation de la présente divulgation divulguent un procédé, un appareil et un dispositif de détection d'un objet lancé de haut, ainsi qu'un support de stockage informatique, consistant à : selon une séquence temporelle d'acquisition d'images, lire séquentiellement deux images adjacentes à détecter à partir de multiples images à détecter et générer une image de flux optique correspondant aux deux images adjacentes par un modèle prédéfini de flux optique ; déterminer des images de flux optique à détecter contenant un objet lancé à partir des multiples images de flux optique et déterminer des coordonnées positionnelles du point central de l'objet lancé dans chaque image de flux optique à détecter ; déterminer une trajectoire de mouvement de l'objet lancé selon les coordonnées positionnelles du point central de l'objet lancé dans chaque image de flux optique à détecter ; et exécuter un traitement de détection pour un objet lancé de haut d'après la trajectoire de mouvement.
PCT/CN2021/123512 2021-06-29 2021-10-13 Procédé, appareil et dispositif de détection d'un objet lancé de haut et support de stockage informatique Ceased WO2023273011A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110729203.4 2021-06-29
CN202110729203.4A CN113409362B (zh) 2021-06-29 2021-06-29 高空抛物检测方法和装置、设备及计算机存储介质

Publications (2)

Publication Number Publication Date
WO2023273011A1 true WO2023273011A1 (fr) 2023-01-05
WO2023273011A9 WO2023273011A9 (fr) 2023-02-09

Family

ID=77680275

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/123512 Ceased WO2023273011A1 (fr) 2021-06-29 2021-10-13 Procédé, appareil et dispositif de détection d'un objet lancé de haut et support de stockage informatique

Country Status (2)

Country Link
CN (1) CN113409362B (fr)
WO (1) WO2023273011A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117934556A (zh) * 2024-03-25 2024-04-26 杭州海康威视数字技术股份有限公司 高空抛物的检测方法、装置、存储介质和电子设备

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113409362B (zh) * 2021-06-29 2023-02-21 深圳市商汤科技有限公司 高空抛物检测方法和装置、设备及计算机存储介质
CN114119653B (zh) * 2021-09-28 2025-04-25 浙江大华技术股份有限公司 抛洒物检测方法、装置、电子装置和存储介质
CN116363543A (zh) * 2021-12-24 2023-06-30 顺丰科技有限公司 高空抛物事件的检测方法及装置
CN114332154B (zh) * 2022-03-04 2022-06-14 英特灵达信息技术(深圳)有限公司 一种高空抛物检测方法及系统
CN114332777B (zh) * 2022-03-08 2022-05-17 南京甄视智能科技有限公司 高空抛物检测方法及装置
CN115100605A (zh) * 2022-07-11 2022-09-23 杭州申昊科技股份有限公司 一种基于机器视觉的动态图像识别方法
CN116052106A (zh) * 2023-01-20 2023-05-02 北京易控智驾科技有限公司 检测掉落物体的方法及电子设备
CN116994201B (zh) * 2023-07-20 2024-03-29 山东产研鲲云人工智能研究院有限公司 对高空抛物进行溯源监测的方法及计算设备
CN118072530B (zh) * 2024-02-19 2024-11-12 安徽大学 一种用于高速公路的车辆异常行为监测系统

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110267461A1 (en) * 2010-05-03 2011-11-03 Stats Llc Trajectory detection and analysis in sporting events
CN111260693A (zh) * 2020-01-20 2020-06-09 北京中科晶上科技股份有限公司 一种高空抛物的检测方法
CN111401311A (zh) * 2020-04-09 2020-07-10 苏州海赛人工智能有限公司 一种基于图像检测的高空抛物识别方法
CN111553274A (zh) * 2020-04-28 2020-08-18 青岛聚好联科技有限公司 一种基于轨迹分析的高空抛物检测方法及装置
CN112037266A (zh) * 2020-11-05 2020-12-04 北京软通智慧城市科技有限公司 一种下落物体的识别方法、装置、终端设备及存储介质
CN112418069A (zh) * 2020-11-19 2021-02-26 中科智云科技有限公司 一种高空抛物检测方法、装置、计算机设备及存储介质
CN113409362A (zh) * 2021-06-29 2021-09-17 深圳市商汤科技有限公司 高空抛物检测方法和装置、设备及计算机存储介质

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8553943B2 (en) * 2011-06-14 2013-10-08 Qualcomm Incorporated Content-adaptive systems, methods and apparatus for determining optical flow
CN110415269B (zh) * 2019-07-19 2023-03-31 浙江大学 一种动静态背景下的目标跟踪算法
CN111814686A (zh) * 2020-07-09 2020-10-23 国网山西省电力公司吕梁供电公司 一种基于视觉的输电线路识别及异物入侵在线检测方法
CN112308000B (zh) * 2020-11-06 2023-03-07 安徽清新互联信息科技有限公司 一种基于时空信息的高空抛物检测方法
CN112733690B (zh) * 2020-12-31 2024-02-20 北京易华录信息技术股份有限公司 一种高空抛物检测方法、装置及电子设备

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110267461A1 (en) * 2010-05-03 2011-11-03 Stats Llc Trajectory detection and analysis in sporting events
CN111260693A (zh) * 2020-01-20 2020-06-09 北京中科晶上科技股份有限公司 一种高空抛物的检测方法
CN111401311A (zh) * 2020-04-09 2020-07-10 苏州海赛人工智能有限公司 一种基于图像检测的高空抛物识别方法
CN111553274A (zh) * 2020-04-28 2020-08-18 青岛聚好联科技有限公司 一种基于轨迹分析的高空抛物检测方法及装置
CN112037266A (zh) * 2020-11-05 2020-12-04 北京软通智慧城市科技有限公司 一种下落物体的识别方法、装置、终端设备及存储介质
CN112418069A (zh) * 2020-11-19 2021-02-26 中科智云科技有限公司 一种高空抛物检测方法、装置、计算机设备及存储介质
CN113409362A (zh) * 2021-06-29 2021-09-17 深圳市商汤科技有限公司 高空抛物检测方法和装置、设备及计算机存储介质

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ZHANG LIPING, FAN HONG; WANG LUYAO; ZHANG YUHAN: "Research on Moving Object Extraction by Optical Flow Method", JISUANJI YU SHUZI GONGCHENG - COMPUTER AND DIGITAL ENGINEERING, ZHONGGUO CHUANBO ZHONGGONG JITUAN GONGSI. DI-709 YANJIUSUO, CN, vol. 48, no. 1, 31 January 2020 (2020-01-31), CN , pages 83 - 87, XP093019542, ISSN: 1672-9722, DOI: 10.3969/j.issn.1672-9722.2020.01.016 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117934556A (zh) * 2024-03-25 2024-04-26 杭州海康威视数字技术股份有限公司 高空抛物的检测方法、装置、存储介质和电子设备

Also Published As

Publication number Publication date
CN113409362A (zh) 2021-09-17
WO2023273011A9 (fr) 2023-02-09
CN113409362B (zh) 2023-02-21

Similar Documents

Publication Publication Date Title
WO2023273011A1 (fr) Procédé, appareil et dispositif de détection d'un objet lancé de haut et support de stockage informatique
WO2023273010A1 (fr) Procédé, appareil et dispositif de détection de jet de déchets par les fenêtres en hauteur et support de stockage informatique
CN114639075B (zh) 一种高空抛物坠物的识别方法、系统及计算机可读介质
EP2798578B1 (fr) Classement d'objet basé sur un regroupement
US9323991B2 (en) Method and system for video-based vehicle tracking adaptable to traffic conditions
CN108052917B (zh) 一种基于新旧时相变化发现的违章建筑自动识别的方法
US10489674B2 (en) Information extraction using image data
EP1766581A1 (fr) Procede de detection par un systeme d'observation d'objets desires dans un environnement tres dynamique
CN106600625A (zh) 检测小型生物的图像处理方法及装置
Varghese et al. Sample-based integrated background subtraction and shadow detection
CN109711256B (zh) 一种低空复杂背景无人机目标检测方法
CN103530893A (zh) 相机抖动场景下基于背景差和运动信息的前景检测方法
CN110490043A (zh) 一种基于区域划分和特征提取的森林烟火检测方法
CN116311212B (zh) 基于高速摄像机实现运动状态下的船号识别方法及装置
CN1266656C (zh) 视频监控系统的智能化报警处理方法
US11227165B2 (en) Automatic lighting and security device
CN102592125A (zh) 基于标准差特征的运动目标检测方法
CN115719362B (zh) 一种高空抛物检测方法、系统、设备及存储介质
CN108932465A (zh) 降低人脸检测误检率的方法、装置及电子设备
CN119785162B (zh) 一种基于多模型融合提升事件检测正确率的方法
CN112862780B (zh) 油烟浓度的识别方法、系统、电子设备和存储介质
CN114511879A (zh) 一种基于vis-ir图像的多源融合人体目标检测方法
HK40051296A (en) High-altitude parabolic detection method, device, equipment and computer storage medium
HK40051297A (en) High-altitude parabolic detection method, device, equipment and computer storage medium
Hu et al. Research on abandoned and removed objects detection based on embedded system

Legal Events

Date Code Title Description
NENP Non-entry into the national phase

Ref country code: DE

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 1205A DATED 10.04.2024)

122 Ep: pct application non-entry in european phase

Ref document number: 21947938

Country of ref document: EP

Kind code of ref document: A1