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Title page for etd-0901111-094051


URN etd-0901111-094051 Statistics This thesis had been viewed 2508 times. Download 1803 times.
Author Kaung-Pen Chou
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2010 Semester 2
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 49
Title Human Action Recognition Using Human Body Signature Based on Hidden Markov Model
Keyword
  • Hidden Markov Model
  • Action recognition
  • Action recognition
  • Hidden Markov Model
  • Abstract This thesis presents a human action recognition method based on Hidden Markov Model (HMM). Two features of the shape contour based histogram and skeleton are extraction and merged into a new feature vector to description a human action. In our propose method, one set of time-sequential images is converted into a sequence of image, and the sequence is transform into a symbol sequence by Euclidean distance. We design a codebook which contains each defined action type and compute the similarity between feature vectors. Each feature vector of the sequence is matched against the codebook and is assigned to the symbol which is most similar. By this way, time-sequential images are transformed into a symbol sequence. We use HMM to model each action type. In the learning phase, the parameters of HMM are optimized so as to best describe to the training sequences. For action recognition, the model which is best match with the sequence is chosen as the recognized type. The experimental results show the effectiveness for action recognizing action. A 92.9% recognition rate is obtained.
    Advisor Committee
  • Chen-Chiung Hsieh - advisor
  • none - co-chair
  • none - co-chair
  • none - co-chair
  • Files indicate accessible at a year
    Date of Defense 2011-07-27 Date of Submission 2011-09-01


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