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BMVC
2010

Toward Robust Action Retrieval in Video

9 years 1 months ago
Toward Robust Action Retrieval in Video
Retrieving human actions from video databases is a paramount but challenging task in computer vision. In this work, we develop such a framework for robustly recognizing human actions in video sequences. The contribution of the paper is twofold. First a reliable neural model, the Multi-level Sigmoidal Neural Network (MSNN) as a classifier for the task of action recognition is presented. Second we unfold how the temporal shape variations can be accurately captured based on both temporal self-similarities and fuzzy log-polar histograms. When the method is evaluated on the popular KTH dataset, an average recognition rate of 94.3% is obtained. Such results have the potential to compare very favorably to those of other investigators published in the literature. Further the approach is amenable for real-time applications due to its low computational requirements.
Samy Bakheet, Ayoub Al-Hamadi, Bernd Michaelis, Us
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where BMVC
Authors Samy Bakheet, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed
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