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

Method for detecting exception target behavior in intelligent vision monitoring

A technology for abnormal detection and intelligent vision, applied in image analysis, instrumentation, computing, etc., to achieve the effect of high level of intelligence, fast detection speed, and fast learning speed

Active Publication Date: 2009-01-14
ZHEJIANG UNIV
View PDF2 Cites 23 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Again, this approach requires pre-configuration of specific behavior

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
  • Method for detecting exception target behavior in intelligent vision monitoring
  • Method for detecting exception target behavior in intelligent vision monitoring
  • Method for detecting exception target behavior in intelligent vision monitoring

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0034] As shown in the figure, a module structure for detecting abnormal target behavior in intelligent visual surveillance includes:

[0035] Target tracking module 110: sampling the image sequence containing the moving target to generate the target motion trajectory;

[0036] Trajectory database 115: store target motion trajectory;

[0037] Preprocessing module 120: use the moving average filter to filter out the noise of the target trajectory, make it smooth, and describe the target trajectory with a spatial descriptor;

[0038] Sub-track extraction module 125: extract the sub-track set of the target motion track, and merge to form a sub-track data set;

[0039] Sub-trajectory pattern network 130: use the first self-organizing feature map network to learn the sub-trajectory data set, and generate a set of sub-trajectory distribution patterns;

[0040] Sub-track anomaly detection information generation module 135: calculate the abnormal threshold of each sub-track distribu...

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 method for detecting abnormal target behavior in intelligent visual surveillance. The method includes two processes, namely, target behavior learning and target behavior detection. The target behavior obtains the abnormal threshold value Epsilon of every sub-track distribution mode and the abnormal threshold value Epsilon of every track distribution mode of target trajectory which describes occurred target behavior; the target detecting processes calculate the distance from the target trajectory which describes the target behavior to be measured to every sub-track distribution mode and every track distribution mode so as to compare the distance with the abnormal threshold value and judge whether the target behavior is abnormal. The method uses the local and integral behavior modes of self-organizing map network acquisition target respectively, which can detect not only the integral abnormal behavior of a target, but also the local abnormal behavior of the target in moving process.

Description

technical field [0001] The invention relates to the technical field of intelligent visual monitoring, in particular to a method for detecting abnormal target behavior in intelligent visual monitoring. Background technique [0002] With the improvement of computer processing capabilities, the development of audio and video codec technology, Internet technology, network multimedia technology and large-capacity storage technology, and the growing needs of security, finance, education and other industries, video surveillance technology has developed rapidly. The intelligent visual monitoring system has the ability to observe and analyze the content of the monitoring scene, and can automatically analyze the video sequences recorded by multiple cameras without or with a small amount of human intervention, thereby replacing humans to complete visual monitoring tasks. [0003] Intelligent visual monitoring includes target detection, target classification, target tracking, target beh...

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): G06T7/20
Inventor 陈耀武曲琳孟旭炯
Owner ZHEJIANG 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