Efficient human action and gait analysis using multiresolution motion energy histogram

Chih Chang Yu, Hsu Yung Cheng, Chien Hung Cheng, Kuo Chin Fan

研究成果: 雜誌貢獻期刊論文同行評審

15 引文 斯高帕斯(Scopus)


Average Motion Energy (AME) image is a good way to describe human motions. However, it has to face the computation efficiency problem with the increasing number of database templates. In this paper, we propose a histogram-based approach to improve the computation efficiency. We convert the human action/gait recognition problem to a histogram matching problem. In order to speed up the recognition process, we adopt a multiresolution structure on the Motion Energy Histogram (MEH). To utilize the multiresolution structure more efficiently, we propose an automated uneven partitioning method which is achieved by utilizing the quadtree decomposition results of MEH. In that case, the computation time is only relevant to the number of partitioned histogram bins, which is much less than the AME method. Two applications, action recognition and gait classification, are conducted in the experiments to demonstrate the feasibility and validity of the proposed approach.

期刊Eurasip Journal on Advances in Signal Processing
出版狀態已出版 - 2010


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