Meng, Hongying and Pears, Nick and Bailey, Chris (2008) Motion feature combination for human action recognition in video. In: Computer Vision and Computer Graphics. Theory and Application. Springer, Berlin Heidelberg, pp. 151-163. ISBN 978-3-540-89681-4

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Abstract

We study the human action recognition problem based on motion features directly extracted from video. In order to implement a fast human action recognition system, we select simple features that can be obtained from non-intensive computation. We propose to use the motion history image (MHI) as our fundamental representation of the motion. This is then further processed to give a histogram of the MHI and the Haar wavelet transform of the MHI. The combination of these two features is computed cheaply and has a lower dimension than the original MHI. The combined feature vector is tested in a Support Vector Machine (SVM) based human action recognition system and a significant performance improvement has been achieved. The system is efficient to be used in real-time human action classification systems.

Item Type:	Book Section
Additional Information:	We study the human action recognition problem based on motion features directly extracted from video. In order to implement a fast human action recognition system, we select simple features that can be obtained from non-intensive computation. We propose to use the motion history image (MHI) as our fundamental representation of the motion. This is then further processed to give a histogram of the MHI and the Haar wavelet transform of the MHI. The combination of these two features is computed cheaply and has a lower dimension than the original MHI. The combined feature vector is tested in a Support Vector Machine (SVM) based human action recognition system and a significant performance improvement has been achieved. The system is efficient to be used in real-time human action classification systems.
Keywords:	Computer vision, Image Processing, Machine Learning
Subjects:	G Mathematical and Computer Sciences > G760 Machine Learning G Mathematical and Computer Sciences > G400 Computer Science G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:	College of Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User:	Users 503819 not found.
Date Deposited:	12 Aug 2009 13:54
Last Modified:	13 Mar 2013 08:33
URI:	http://eprints.lincoln.ac.uk/id/eprint/1977

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