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    National Tsing Hua University Institutional Repository > 電機資訊學院 > 電機工程學系 > 期刊論文 >  Skeleton-based walking motion analysis using hidden Markov model and active shape models


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/71963


    Title: Skeleton-based walking motion analysis using hidden Markov model and active shape models
    Authors: I-Cheng Chang;Chung-Lin Huang
    教師: 黃仲陵
    Date: 2001
    Publisher: Institute of Information Science Academia Sinica
    Relation: Journal of Information Science and Engineering, Institute of Information Science Academia Sinica, Volume 17,   Issue 3,   May 2001, Pages 371-403
    Keywords: hidden Markov models
    image recognition
    image sequences
    image thinning
    motion estimation
    parameter estimation
    statistical analysis
    Abstract: This paper proposes a skeleton-based human walking motion analysis system which consists of three major phases. In the first phase, it extracts the human body skeleton from the background and then obtains the body signatures. In the second phase, it analyzes the training sequences to generate statistical models. In the third phase, it uses the trained models to recognize the input human motion sequence and calculate the motion parameters. The experimental results demonstrate how our system can recognize the motion type and describe the motion characteristics of the image sequence. Finally, the synthesized motion sequences are illustrated. The major contributions of this paper are: (1) development of a skeleton-based method and use of hidden Markov models (HMM) to recognize the motion type; (2) incorporation of the active shape models (ASM) and the body structure characteristics to generate the motion parameter curves of the human motion
    Relation Link: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.98.1460
    http://www.iis.sinica.edu.tw/
    URI: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/71963
    Appears in Collections:[電機工程學系] 期刊論文

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