- Research Article
- Open access
- Published:
An Action Recognition Scheme Using Fuzzy Log-Polar Histogram and Temporal Self-Similarity
EURASIP Journal on Advances in Signal Processing volume 2011, Article number: 540375 (2011)
Abstract
Temporal shape variations intuitively appear to provide a good cue for human activity modeling. In this paper, we lay out a novel framework for human action recognition based on fuzzy log-polar histograms and temporal self-similarities. At first, a set of reliable keypoints are extracted from a video clip (i.e., action snippet). The local descriptors characterizing the temporal shape variations of action are then obtained by using the temporal self-similarities defined on the fuzzy log-polar histograms. Finally, the SVM classifier is trained on these features to realize the action recognition model. The proposed method is validated on two popular and publicly available action datasets. The results obtained are quite encouraging and show that an accuracy comparable or superior to that of the state-of-the-art is achievable. Furthermore, the method runs in real time and thus can offer timing guarantees to real-time applications.
Publisher note
To access the full article, please see PDF.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Sadek, S., Al-Hamadi, A., Michaelis, B. et al. An Action Recognition Scheme Using Fuzzy Log-Polar Histogram and Temporal Self-Similarity. EURASIP J. Adv. Signal Process. 2011, 540375 (2011). https://doi.org/10.1155/2011/540375
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1155/2011/540375