Modeling Supporting Regions for Close Human Interaction Recognition

Abstract

This paper addresses the problem of recognizing human interactions with close physical contact from videos. Different from conventional human interaction recognition, recognizing close interactions faces the problems of ambiguities in feature-to-person assignments and frequent occlusions. Therefore, it is infeasible to accurately extract the interacting people, and the recognition performance of an interaction model is degraded. We propose a patch-aware model to overcome the two problems in close interaction recognition. Our model learns discriminative supporting regions for each interacting individual. The learned supporting regions accurately extract individuals at patch level, and explicitly indicate feature assignments. In addition, our model encodes a set of body part configurations for one interaction class, which provide rich representations for frequent occlusions. Our approach is evaluated on the UT-Interaction dataset and the BIT-Interaction dataset, and achieves promising results.