Wavelet mach filter for omnidirectional human activity recognition
Document Type
Article
Publication Date
1-1-2012
Abstract
Action recognition is important in the eld of intelligent security and surveil- lance. However, most surveillance cameras can only capture in one direction with limited viewing angle. This paper proposes an edge enhancement template-based method of omnidirectional action recognition that is able to detect specic actions at a 360 degree of view. A MACH lter captures intra-class variability by synthesizing a single action MACH lter for a given action class. The proposed method, based on the wavelet MACH lter, provides additional exibility of an adaptive choice of wavelet scale factors and, in doing so, enables the selection of the size and orientation of the smoothing function in edge enhancement to optimize the performance of the MACH lter. Moreover, the use of wavelet transform improves the performance of the MACH lter by enhancing the cross-correlation peak intensity in the recognition process. The unwarping of an omnidirectional image into a panoramic image further enables action recognition in 360 degree wide angle of view.
Keywords
Omnidirectional vision, Log-polar transformation, Action recognition, Max- imum average correlation height, Mexican-hat wavelet, 3D normalized cross-correlation
Divisions
ai
Publication Title
International Journal of Innovative Computing, Information and Control
Volume
8
Issue
5
Additional Information
Department of Artificial Intelligence, Faculty of Computer Science & Information Technology Building, University of Malaya, 50603 Kuala Lumpur, MALAYSIA