Motion feature combination for human action recognition in video

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

Documents
Motion feature combination for human action recognition in video
[img]
[Download]
[img] PDF
visapp_v4.pdf
Restricted to Repository staff only

392Kb

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 Science > School of Computer Science
ID Code:1977
Deposited By:INVALID USER
Deposited On:12 Aug 2009 13:54
Last Modified:13 Mar 2013 08:33

Repository Staff Only: item control page