Action recognition using Correlogram of Body Poses and spectral regression

Human action recognition is an important topic in computer vision with its applications in robotics, video surveillance, human-computer interaction, user interface design, and multimedia video retrieval, etc. In this paper, we propose a novel representation for human actions using Correlogram of Body Poses (CBP) which takes advantage of both the probabilistic distribution and the temporal relationship of human poses. To reduce the high dimensionality of the CBP representation, an efficient subspace learning technique called Spectral Regression Discriminant Analysis (SRDA) is explored. Experimental results on the challenging IXMAS dataset show that the proposed algorithm outperforms the state-of-the-art methods on action recognition.