Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Cluster optical flow characteristic-based abnormity behavior detection method, system and device

A detection method and optical flow technology, applied in the field of computer vision, can solve problems such as large amount of calculation, poor real-time performance, and poor adaptability, and achieve the effect of solving large amount of calculation and reducing the amount of calculation

Active Publication Date: 2018-05-18
SHENZHEN UNIV
View PDF5 Cites 19 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The technical problem mainly solved by the present invention is to provide a method for abnormal behavior detection based on clustering optical flow characteristics, which can solve technical problems such as large amount of calculation, poor real-time performance and poor adaptability

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Cluster optical flow characteristic-based abnormity behavior detection method, system and device
  • Cluster optical flow characteristic-based abnormity behavior detection method, system and device
  • Cluster optical flow characteristic-based abnormity behavior detection method, system and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] Hereinafter, exemplary embodiments of the present application will be described with reference to the accompanying drawings. Well-known functions and constructions are not described in detail for clarity and conciseness. Terms described below, which are defined in consideration of functions in the present application, may vary according to user and operator's intention or implementation. Therefore, the terms should be defined on the basis of the disclosure throughout the specification.

[0025] see figure 1 , is a schematic flowchart of the first embodiment of the video surveillance method based on video structured data and deep learning in the present invention. The method includes:

[0026] S10: Read the video.

[0027] Optionally, reading the video includes reading real-time video collected by the camera and / or pre-recorded and saved video data. Wherein, the camera for collecting real-time video may be one of a USB camera and a network camera based on rtsp proto...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a cluster optical flow characteristic-based abnormity behavior detection method comprising the following steps: using a depth learning target detection method to obtain a target position area; obtaining the optical flow information of the target areas in at least two adjacent frame images; building a space-time model for the extracted optical flow information of the targetareas; obtaining the average information entropy of the space-time model corresponding to each detection target; clustering the optical flow points in the space-time model so as to obtain cluster optical flow points and the average kinetic energy of the cluster points; determining whether the target has abnormal behaviors, such as fighting and running, or not according to the cluster point averagekinetic energy, the information entropy and the deviation of a pre-trained normal model. The method can accurately and fast detect abnormity video frames and abnormal targets; the invention also provides an abnormity behavior detection system and device applied to the intelligent video analysis field, thus providing very well practical values.

Description

technical field [0001] The present invention relates to the field of computer vision, in particular to an abnormal behavior detection method, system and device based on clustering optical flow features. Background technique [0002] With the development of intelligent monitoring technology, the analysis of abnormal behavior in the field of computer vision is particularly important. In the prior art, the optical flow method is often used to analyze abnormal behaviors. Generally speaking, the commonly used optical flow methods mainly include Horn-Schunck (HS) optical flow and Lucas-Kanade (LK) optical flow. HS optical flow calculates the optical flow vectors of all pixels in a frame of image, so the time overhead of this algorithm is relatively large, and the real-time performance is poor; the existing LK optical flow method is based on local constraints, which can solve sparse The calculation of optical flow (that is, the calculation of optical flow of a specified set of pi...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06T7/269
CPCG06T7/269G06V40/20G06V20/40G06V20/54G06F18/214
Inventor 谢维信王鑫高志坚
Owner SHENZHEN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com