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2013 (Vol. 5, Issue: 21)
Article Information:

Multi-Features Encoding and Selecting Based on Genetic Algorithm for Human Action Recognition from Video

Chenglong Yu, Xuan Wang, Muhammad Waqas Anwar and Kai Han
Corresponding Author:  Chenglong Yu 

Key words:  Feature encoding, feature selecting, genetic algorithm, human action recognition, multi-features, ,
Vol. 5 , (21): 5128-5132
Submitted Accepted Published
November 24, 2012 January 05, 2013 May 20, 2013
Abstract:

In this study, we proposed multiple local features encoded for recognizing the human actions. The multiple local features were obtained from the simple feature description of human actions in video. The simple features are two kinds of important features, optical flow and edge, to represent the human perception for the video behavior. As the video information descriptors, optical flow and edge, which their computing speeds are very fast and their requirement of memory consumption is very low, can represent respectively the motion information and shape information. Furthermore, key local multi-features are extracted and encoded by GA in order to reduce the computational complexity of the algorithm. After then, the Multi-SVM classifier is applied to discriminate the human actions.
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  Cite this Reference:
Chenglong Yu, Xuan Wang, Muhammad Waqas Anwar and Kai Han, 2013. Multi-Features Encoding and Selecting Based on Genetic Algorithm for Human Action Recognition from Video.  Research Journal of Applied Sciences, Engineering and Technology, 5(21): 5128-5132.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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