Decreasing the Number of Features for Improving Human Action Classification

Abstract : Action classification in videos has been a very active field of research over the past years. Human action classification is a research field with application to various areas such as video indexing, surveillance, human-computer interfaces, among others. In this paper, we propose a strategy based on decreasing the number of features in order to improve accuracy in the human action classification task. Thus, to classify human action, we firstly compute a video segmentation for simplifying the visual information, in the following, we use a mid-level representation for representing the feature vectors which are finally classified. Experimental results demonstrate that our approach has improved the quality of human action classification in comparison to the baseline while using 60% less features.
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Jacques de Souza Kleber, Arnaldo de Albuquerque Araújo, Zenilton Kleber G. Do Parocinio Jr, Jean Cousty, Laurent Najman, et al.. Decreasing the Number of Features for Improving Human Action Classification. 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI 2016), Oct 2016, Sao Paulo, Brazil. ⟨10.1109/SIBGRAPI.2016.035⟩. ⟨hal-01616376⟩

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