3D Activity Recognition Using Motion History and Binary Shape Templates

This paper presents our work on activity recognition in 3D depth images. We propose a global descriptor that is accurate, compact and easy to compute as compared to the state-of-the-art for characterizing depth sequences. Activity enactment video is divided into temporally overlapping blocks. Each block (set of image frames) is used to generate Motion History Templates (MHTs) and Binary Shape Templates (BSTs) over three different views - front, side and top. The three views are obtained by projecting each video frame onto three mutually orthogonal Cartesian planes. MHTs are assembled by stacking the difference of consecutive frame projections in a weighted manner separately for each view. Histograms of oriented gradients are computed and concatenated to represent the motion content. Shape information is obtained through a similar gradient analysis over BSTs. These templates are built by overlaying all the body silhouettes in a block, separately for each view. To effectively trace shape-growth, BSTs are built additively along the blocks.

Consequently, the complete ensemble of gradient features carries both 3D shape and motion information to effectively model the dynamics of an articulated body movement. Experimental results on 4 standard depth databases (MSR 3D Hand Gesture, MSR Action, Action-Pairs, and UT-Kinect) prove the efficacy as well as the generality of our compact descriptor. Further, we successfully demonstrate the robustness of our approach to (impulsive) noise and occlusion errors that commonly affect depth data.