Table of Contents
ISRN Machine Vision
Volume 2013, Article ID 215195, 7 pages
http://dx.doi.org/10.1155/2013/215195
Research Article

Affine-Invariant Feature Extraction for Activity Recognition

1Department of Mathematics and Computer Science, Faculty of Science, Sohag University, 82524 Sohag, Egypt
2Institute for Information Technology and Communications (IIKT), Otto von Guericke University Magdeburg, 39106 Magdeburg, Germany

Received 28 April 2013; Accepted 4 June 2013

Academic Editors: A. Gasteratos, D. P. Mukherjee, and A. Torsello

Copyright © 2013 Samy Sadek et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose an innovative approach for human activity recognition based on affine-invariant shape representation and SVM-based feature classification. In this approach, a compact computationally efficient affine-invariant representation of action shapes is developed by using affine moment invariants. Dynamic affine invariants are derived from the 3D spatiotemporal action volume and the average image created from the 3D volume and classified by an SVM classifier. On two standard benchmark action datasets (KTH and Weizmann datasets), the approach yields promising results that compare favorably with those previously reported in the literature, while maintaining real-time performance.