Human action recognition using depth maps

In this paper we propose an approach to recognize human actions using depth images. Here, we capture the motion dynamics of the object from the depth difference image and average depth image. The features from the space-time depth difference images are obtained from hierarchical division of the silhouette bounding box. We also make use of motion history images to represent the temporal information about the action. We make use of the translation, scale and orientation invariant Hu moments to represent the features of the motion history image and the average depth image. We then classify human actions using support vector machines. We analyze the representation efficiency of Hu moments and the hierarchical division of bounding boxes separately in order to evaluate the contribution of each of the features. The results show superior performance of over 90% when both features are combined.