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

Chaotic characteristic parameter-based motion mode video segmentation and traffic condition identification method

A technology of chaotic features and traffic conditions, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as large Lyapunov index, not suitable for video segmentation, etc., achieve simple algorithm, broad market prospects and application value Effect

Inactive Publication Date: 2013-02-13
SHANGHAI JIAO TONG UNIV
View PDF1 Cites 14 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But the maximum Lyapunov index is not suitable for video segmentation

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
  • Chaotic characteristic parameter-based motion mode video segmentation and traffic condition identification method
  • Chaotic characteristic parameter-based motion mode video segmentation and traffic condition identification method
  • Chaotic characteristic parameter-based motion mode video segmentation and traffic condition identification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] In order to better understand the technical scheme of the present invention,

[0027] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following the described embodiment.

[0028] The present invention comprises the following steps:

[0029] (1) Calculate the eigenvector matrix

[0030] Such as figure 1 As shown, in this embodiment, the feature value of each pixel point that changes with time is obtained first. Then the feature quantity is composed into a feature vector, and each pixel in the video is represented by this feature vector. Thus turning the entire video into a matrix of feature vectors. Let us first introduce the basic concepts of chao...

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 chaotic characteristic parameter-based motion mode video segmentation and traffic condition identification method, which comprises the following steps of: 1, calculating a characteristic vector matrix; 2, segmenting a video; 3, comparing clustering results; 4, retrieving the video; and 5, classifying the video. Characteristic parameters of the video are extracted, and new characteristic vectors are formed, so that the dynamic information of the video can be well described, and the method can be widely applied to various civil and military systems such as video monitoring systems, video conference systems, industrial product detection systems, robot visual navigation systems and military target detection and classification systems, and has broad market prospect and high application value.

Description

technical field [0001] The invention relates to a classification method in the technical field of computer pattern recognition, in particular to an algorithm for video segmentation and traffic condition recognition based on chaotic feature quantities. Background technique [0002] Video segmentation is a research hotspot in the field of computer vision and pattern recognition. Accurately classifying different motion patterns in videos has broad application prospects in both civilian and military applications. For example, through video surveillance, different traffic conditions (congestion, moderate congestion, less traffic flow) are classified. Aiming at the problem of video segmentation, scholars at home and abroad have proposed many methods. [0003] The main methods of video segmentation are based on motion information, based on models and based on spatio-temporal information. Based on motion information, there are optical flow method and change detection method. The...

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): G06K9/62
Inventor 胡士强王勇
Owner SHANGHAI JIAO TONG 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