NDLI: Human behavior recognition from arbitrary views

Content Provider	IEEE Xplore Digital Library
Author	Chi-Hung Chuang Jun-Wei Hsieh Yi-Da Chiou I-Ru Tsay Ming-Hui Jin
Copyright Year	2010
Description	Author affiliation: Dept. of Electrical Engineering, YuanZe University, Taiwan, R.O.C. (Yi-Da Chiou) \|\| Institute of Information Industry, Taipei, Taiwan, R.O.C. (I-Ru Tsay; Ming-Hui Jin) \|\| Department of Learning and Digital Technology, Fo Guang University, Yilan, Taiwan, R.O.C. (Chi-Hung Chuang) \|\| Dept. of Computer Information Engineering, National Taiwan Ocean University, Taiwan, R.O.C. (Jun-Wei Hsieh)
Abstract	This paper presents a new behavior classification system that can analyze human behaviors from arbitrary views. Technically, if different viewing angle are used for observing a person, his appearances will change significantly. To freely recognize his behaviors, traditional methods tend to adopt 3-D data for behavior analysis. However, its inherent correspondence process will make it inappropriate for real time applications. To tackle this problem, a novel view alignment method is first proposed for mapping each action sequence to a fixed view. To achieve this mapping, two features extracted from spatial and temporal domains are used for representing each action sequence. For the spatial feature, the "centroid context" of each posture is defined and extracted through a triangulation technique. For the temporal feature, the "posture transition probability" is constructed for recording the probabilities of one posture type transferring to another one. After mapping, a novel matrix representation is proposed for describing each action more accurately. After that, the Viterbi algorithm is used for aligning two action sequences and then classifying them to different behavior types.
Starting Page	2654
Ending Page	2657
File Size	328082
Page Count	4
File Format	PDF
ISBN	9781424453085
DOI	10.1109/ISCAS.2010.5537053
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-30
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Information analysis Image analysis Flowcharts Surveillance Hidden Markov models Motion analysis Image recognition Support vector machines Support vector machine classification
Content Type	Text
Resource Type	Article

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