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Published in:
2010 | OriginalPaper | Chapter
Pedestrian Gait Classification Based on Hidden Markov Models
Authors : Weihua Wang, Zhijing Liu
Published in: Artificial Intelligence and Computational Intelligence
Publisher: Springer Berlin Heidelberg
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Analysis of human activity from video sequences is one of the hottest and difficult research areas in computer visions. Because of the fact that human continuous motion can be decomposed into an image sequence based on time, state space method is applied in this paper. First, Silhouettes are extracted using the Background Subtraction method and features are represented by moment. Then a method using recursion method for establishment of the standard gait state sequence is proposed. In order to determine whether the behavior is abnormal in different scenarios, wavelet moment is used to extract features of the human body images, and then recognizes the moving human bodies activity based on Discrete Hidden Markov Model. The experiment tests show some encouraging results also indicates the algorithm has very small leak-examining and mistake-examining-rate which indicate that the method could be a choice for solving the problem but more tests are required.