Combining depth-skeleton feature with sparse coding for action recognition

RGB-D human action recognition is a very active research topic in computer vision and robotics. In this paper, an action recognition method that combines gradient information and sparse coding is proposed. First of all, we leverage depth gradient information and distance of skeleton joints to extract coarse Depth-Skeleton (DS) feature. Then, the sparse coding and max pooling are combined to refine the coarse DS feature. Finally, the Random Decision Forests (RDF) is utilized to perform action recognition. Experimental results on three public datasets show the superior performance of our method.