An efficient way to classify human gaits

The computation efficiency in human identification problem is a very important issue when the number of database templates is large. In this paper, we propose a histogram based approach to improve the computation efficiency for human gait classification. We convert the human gait classification 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. Experiments demonstrate the feasibility and validity of the proposed approach.