In this paper, 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 processed MHI thus allows a combined feature vector to be computed cheaply and this has a lower dimension than the original MHI. Finally, this feature vector is used in a SVM-based human action recognition system. Experimental results demonstrate the method to be efficient, allowing it to be used in real-time human action classification systems.
|Published - 2007
|2nd International Conference on Computer Vision Theory and Applications - Barcelona, Spain
Duration: 8 Mar 2007 → 11 Mar 2007
|2nd International Conference on Computer Vision Theory and Applications
|8/03/07 → 11/03/07