Motion analysis for human interaction detection using optical flow on lattice superpixels

We develop a new video-based motion analysis algorithm to determine whether two persons have any interaction in their meeting. The interaction between two persons can be very general, such as shaking hands, exchanging objects, and so on. To make the motion analysis robust to image noise, we segment each video frame into a set of superpixels and then derive a motion feature and a motion pattern for each superpixel by averaging the optical flow within the superpixel. Specifically, we use the lattice cut to construct the superpixels, which are spatially and temporally consistent across frames. Based on the motion feature and the motion pattern of the superpixels, we develop an algorithm to divide an input video sequence into three consecutive periods: 1) two persons walking toward each other, 2) two persons meeting each other, and 3) two persons walking away from each other. The experiment show that the proposed algorithm can accurately distinguish the videos with and without human interactions.