Skip to content

Advertisement

  • Research Article
  • Open Access

An Action Recognition Scheme Using Fuzzy Log-Polar Histogram and Temporal Self-Similarity

  • 1Email author,
  • 1,
  • 1 and
  • 2
EURASIP Journal on Advances in Signal Processing20112011:540375

https://doi.org/10.1155/2011/540375

  • Received: 25 July 2010
  • Accepted: 8 January 2011
  • Published:

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.

Keywords

  • Quantum Information
  • Activity Modeling
  • Video Clip
  • Action Recognition
  • Full Article

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
Institute for Electronics, Signal Processing and Communications (IESK), Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany
(2)
Electrical Engineering Department, Assiut University, Assiut, Egypt

Copyright

© Samy Sadek et al. 2011

This article is published under license to BioMed Central Ltd. 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.

Advertisement