Table of Contents
ISRN Machine Vision
Volume 2012, Article ID 872131, 9 pages
Research Article

Chord-Length Shape Features for Human Activity Recognition

Institute for Electronics, Signal Processing and Communications (IESK), Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany

Received 2 August 2012; Accepted 20 September 2012

Academic Editors: M. La Cascia, A. Prati, J. M. Tavares, and C. S. Won

Copyright © 2012 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.


Despite their high stability and compactness, chord-length shape features have received relatively little attention in the human action recognition literature. In this paper, we present a new approach for human activity recognition, based on chord-length shape features. The most interesting contribution of this paper is twofold. We first show how a compact, computationally efficient shape descriptor; the chord-length shape features are constructed using 1-D chord-length functions. Second, we unfold how to use fuzzy membership functions to partition action snippets into a number of temporal states. On two benchmark action datasets (KTH and WEIZMANN), the approach yields promising results that compare favorably with those previously reported in the literature, while maintaining real-time performance.