Efficient Human Action and Gait Analysis Using Multiresolution Motion Energy Histogram
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Research Article Efficient Human Action and Gait Analysis Using Multiresolution Motion Energy Histogram Chih-Chang Yu,1 Hsu-Yung Cheng,2 Chien-Hung Cheng,2 and Kuo-Chin Fan2, 3 1 Department
of Computer Science and Information Engineering, Vanung University, Chung-Li 32061, Taiwan of Computer Science and Information Engineering, National Central University, Chung-Li 32001, Taiwan 3 Department of Informatics, Fo-Guang University, I-Lan 26247, Taiwan 2 Department
Correspondence should be addressed to Chih-Chang Yu, [email protected] Received 29 November 2009; Accepted 10 February 2010 Academic Editor: Jenq-Neng Hwang Copyright © 2010 Chih-Chang Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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.
1. Introduction Analyzing human’s behavior or identity is a very interesting research topic because human is usually the most concerned object in many applications such as surveillance system or video understanding. Recently, this problem is usually solved by two kinds of approaches: video-based approaches or sensor-based approaches [1, 2]. The advantage of videobased approach is that the individuals do not have to put on additional devices and the hardware cost is also cheaper. For video-based approaches, there exists abundant considerable works made by previous researchers such as employing template matching [3, 4], Intensity-based features [5, 6], shape matching [7], and spatial-temporal features [8–13]. For spatial-temporal features, motion energy images (MEIs) are a very useful feature which incorporates temporal information into spatial images. The idea of MEI was firstly introduced by Bobick and Davis in [14]. The authors obtained the MEI by collecting a group of frames and extract scale invariant features for recognition. This idea was extended to the so called average motion energy (AME) by aligning and
normalizing the foreground silhouettes [15]. By doing so, the AME can depict human’s motion i
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