Abstract: This paper proposes a method to human action recognition from RGB video clips. The method is based on capturing the local motion information from smaller size video clips. Local motion information is captured through accumulation of motion in different shape and size of patches of spatial domain. The motion information is then transformed to motion histograms. Further, all the histograms are concatenated to make the proposed feature vector. Bagging ensemble technique, in form of random forest, is used for classification. The idea is further extended to real time human action recognition mechanism. To show the robustness and efficiency of proposed algorithm, it is performed on publicly available human action datasets Joint-annotated Human Motion Data Base (JHMDB)  and University of Rzeszów (UR) Fall detection dataset . The results are also compared with other state of art methods.
Keywords: Human action recognition, histogram, random forest, RGB camera, real time fall detection