Layered observation vector decomposed hidden Markov model-based method for identifying behaviors

A technology for observing vectors and identifying methods, used in character and pattern recognition, image analysis, image data processing, etc.

Inactive Publication Date: 2010-06-16
BEIJING JIAOTONG UNIV
View PDF0 Cites 36 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention aims at the deficiencies of current multi-person behavior recognition methods in video, and provides a behavior recognition method based on Hidden Markov Model of Hierarchical Observation Vector Decomposition, which can realize multi-perso

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
  • Layered observation vector decomposed hidden Markov model-based method for identifying behaviors
  • Layered observation vector decomposed hidden Markov model-based method for identifying behaviors
  • Layered observation vector decomposed hidden Markov model-based method for identifying behaviors

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0063] figure 1 : the schematic flow chart of the present invention, in order to provide a clear description, we will take the identification of 5 kinds of multi-person behaviors as an example, but the scope of application of the present invention is not limited to these 5 kinds of behaviors. In the summary of the invention, we mentioned that compared with the traditional model, the hidden Markov model based on hierarchical observation vector decomposition in the present invention has no requirement on the number of targets, while the traditional model needs to limit the number of targets. Therefore, we choose the situation when the number of targets is 2 and 3 for identification to illustrate the advantages based on the present invention. Suppose we want to identify the following 5 behaviors:

[0064] Behavior 1: Two targets meet head-on;

[0065] Behavior 2: One target follows another;

[0066] Behavior 3: One target pushes another target down;

[0067] Behavior 4: The f...

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 relates to a layered observation vector decomposed hidden Markov model-based method for identifying behaviors. The method comprises the following aspects of: target detection, target tracking, characteristic extraction, motion modeling, behavior identification and the like. The method improves the conventional hidden Markov model by aiming at deficiencies of a present method for identifying behaviors of multi-user, separates an individual state and an interactive state in the model to stand out interactive relation among targets, and reduces calculated amount by decomposing observation nodes. Meanwhile, the model allows the number of the targets taking the movement to be changed, so that the method has more flexibility in characteristic selection problem than the traditional method and allows simultaneously using discrete characteristics and continuous characteristics. The method plays an important role in analyzing the multi-user interactive behaviors in the fields of video monitoring, and content-based video retrieval and the like.

Description

technical field [0001] The present invention relates to a human body behavior recognition method in video, in particular to a multi-person behavior recognition method based on a hierarchical observation vector decomposition hidden Markov model, which is applied to pattern recognition, artificial intelligence, computer vision, image processing and other technologies field. Background technique [0002] In recent years, video surveillance technology has attracted the attention of the society, especially since the "911" terrorist attacks, Madrid bombings and London bombings, the research and development of automatic video surveillance products for computers has become more urgent. Now, cameras can be seen everywhere in many communities, campuses, and streets. However, the current video surveillance technology is far from intelligent enough, and the monitoring work is basically done manually. However, due to factors such as human energy, physical strength, and labor costs, 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
IPC IPC(8): G06K9/62G06T7/20
Inventor 苗振江郭萍邓海峰
Owner BEIJING JIAOTONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap