Discriminative Dictionary Learning for Skeletal Action Recognition

Human action recognition is an important yet challenging task. With the introduction of RGB-D sensors, human body joints can be extracted with high accuracy, and skeleton-based action recognition has been investigated and gained some success. In this paper, we split an entire action trajectory into several segments and represent each segment using covariance descriptor of joints' coordinates. We further employ the projective dictionary pair learning (PDPL) and majority-voting for multi-class action classification. Experimental results on two benchmark datasets demonstrate the effectiveness of our approach.