NDLI: Human Behavior Analysis Using Deformable Triangulations

Content Provider	IEEE Xplore Digital Library
Author	Yung-Tai Hsu Jun-Wei Hsieh Hai-Feng Kao Liao, H.-Y.M.
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng., Yuan-Ze Univ., Chung-li (Yung-Tai Hsu; Jun-Wei Hsieh)
Abstract	This paper presents a new posture classification system to analyze different human behaviors directly from video sequences using the technique of triangulation. For well analyzing each posture in the video sequences, we propose a triangulation-based method to triangulate it to different triangle meshes from which two important posture features are then extracted, i.e., the ones of skeleton and centroid context. The first one is used for a coarse search and the second one is for a finer classification to classify postures in more details. For the first descriptor, we take advantages of a dfs (depth-first search) scheme to extract the skeleton features of a posture from its triangulation result. Then, with the help of skeleton information, we can define a new shape descriptor, i.e., centroid context, to describe a posture up to a semantic level. That is, the centroid context is a finer descriptor to describe a posture not only from its whole shape but also from its body parts. Since the two descriptors are complement to each other, all desired human postures can be compared and classified very accurately. The nice ability of posture classification can help us generate a set of key postures for transferring a behavior sequence to a set of symbols. Then, a novel string matching scheme is proposed to analyze different human behaviors. Experimental results have proved that the proposed method is robust, accurate, and powerful in human behavior analysis
Starting Page	1
Ending Page	4
File Size	5285192
Page Count	4
File Format	PDF
ISBN	0780392884
DOI	10.1109/MMSP.2005.248629
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Skeleton Hidden Markov models Biological system modeling Video sequences Feature extraction Shape Robustness Surveillance Information science
Content Type	Text
Resource Type	Article

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