Decreasing the Number of Features for Improving Human Action Classification

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.