Paper Info
Title
Efficient human action and gait analysis using multiresolution motion energy histogram
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.
Year
DOI
Venue
2010
10.1155/2010/975291
EURASIP J. Adv. Sig. Proc.
Keywords
Field
DocType
efficient human action,histogram matching problem,multiresolution motion energy histogram,gait analysis,action recognition,multiresolution structure,ame method,gait recognition problem,computation time,recognition process,computation efficiency problem,average motion energy,computation efficiency
Computer vision,Histogram,Computer science,Action recognition,Histogram matching,Gait analysis,Artificial intelligence,Balanced histogram thresholding,Machine learning,Quadtree decomposition,Computation,Speedup
Journal
Volume
Issue
ISSN
2010,
1
1687-6180
Citations 
PageRank 
References 
3
0.40
23
Authors
4
Name
Order
Citations
PageRank
Chih-Chang Yu1328.93
Hsu-Yung Cheng224323.56
Chien-Hung Cheng370.80
Kuo-chin Fan41369117.82