Efficient human action and gait analysis using multiresolution motion energy histogram

  • Authors:

  • Chih-Chang Yu;Hsu-Yung Cheng;Chien-Hung Cheng;Kuo-Chin Fan

  • Affiliations:

  • Department of Computer Science and Information Engineering, Vanung University, Chung-Li , Taiwan;Department of Computer Science and Information Engineering, National Central University, Chung-Li , Taiwan;Department of Computer Science and Information Engineering, National Central University, Chung-Li , Taiwan;Department of Computer Science and Information Engineering, National Central University,hung-Li , Taiwan and Department of Informatics, Fo-Guang University, I-Lan , Taiwan

  • Venue:
  • EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.