Towards human behavior recognition based on spatio temporal features and support vector machines

17 March 2017 Towards human behavior recognition based on spatio temporal features and support vector machines

Sawsen Ghabri, Wael Ouarda, Adel M. Alimi

Author Affiliations +

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103410E (2017) https://doi.org/10.1117/12.2269060
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

Abstract

Security and surveillance are vital issues in today’s world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

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Sawsen Ghabri, Wael Ouarda, and Adel M. Alimi "Towards human behavior recognition based on spatio temporal features and support vector machines", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410E (17 March 2017); https://doi.org/10.1117/12.2269060

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