Recognition of human interaction using multiple features in gray scale images

This paper presents a recognition system that classifies four kinds of human interactions: shaking hands, pointing at the opposite person, standing hand-in-hand, and an intermediate/transitional state between them. Our system achieves recognition by applying the K-nearest neighbor classifier to the parametric human-interaction model, which describes the interpersonal configuration with multiple features from gray scale images (i.e., binary blob, silhouette contour, and intensity distribution). Unlike the algorithms that use temporal information about motion, our system independently classifies each frame by estimating the relative poses of the interacting persons. The system provides a tool to detect the initiation and the termination of an interaction with no parsing procedure for sequential data. Experimental results are presented and illustrated