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


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.

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Correspondence to Samy Sadek.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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).

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