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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Author: search.filters.author.Liang, Yu-MingSubject: search.filters.subject.human behavior analysis

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    A language modeling approach to atomic human action recognition
    (2007)
    Liang, Yu-Ming
    ;
    Shih, Sheng-Wen
    ;
    Shih, Arthur Chun-Chieh
    ;
    Liao, Hong-Yuan Mark
    ;
    Lin, Cheng-Chung
    Visual analysis of human behavior has generated considerable interest in the field of computer vision because it has a wide spectrum of potential applications. Atomic human action recognition is an important part of a human behavior analysis system. In this paper, we propose a language modeling framework for this task. The framework is comprised of two modules: a posture labeling module, and an atomic action learning and recognition module. A posture template selection algorithm is developed based on a modified shape context matching technique. The posture templates form a codebook that is used to convert input posture sequences into training symbol sequences or recognition symbol sequences. Finally, a variable-length Markov model technique is applied to learn and recognize the input symbol sequences of atomic actions. Experiments on real data demonstrate the efficacy of the proposed system.
  • Thumbnail Image
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    Learning Atomic Human Actions Using Variable-Length Markov Models
    (2009-02-01)
    Liang, Yu-Ming
    ;
    Shih, Sheng-Wen
    ;
    Shih, Arthur Chun-Chieh
    ;
    Liao, Hong-Yuan Mark
    ;
    Lin, Cheng-Chung
    Visual analysis of human behavior has generated considerable interest in the field of computer vision because of its wide spectrum of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a basic and complete movement. Learning and recognizing atomic human actions are essential to human behavior analysis. In this paper, we propose a framework for handling this task using variable-length Markov models (VLMMs). The framework is comprised of the following two modules: a posture labeling module and a VLMM atomic action learning and recognition module. First, a posture template selection algorithm, based on a modified shape context matching technique, is developed. The selected posture templates form a codebook that is used to convert input posture sequences into discrete symbol sequences for subsequent processing. Then, the VLMM technique is applied to learn the training symbol sequences of atomic actions. Finally, the constructed VLMMs are transformed into hidden Markov models (HMMs) for recognizing input atomic actions. This approach combines the advantages of the excellent learning function of a VLMM and the fault-tolerant recognition ability of an HMM. Experiments on realistic data demonstrate the efficacy of the proposed system.