Motion histogram quantification for human action recognition

In this paper, we propose an approach for human activity categorizing based on the use of optical flow direction and magnitude features. The main contribution of this paper is the feature representation that mirrors the geometry of the human body and relationships between its moving regions when performing activities. The features are quantified using a quantization algorithm. We analyze the performance of two well-known classifiers: the Naïve Bayes and the SVM. The results show the effectiveness of our approach.