Multiview Skeletal Interaction Recognition Using Active Joint Interaction Graph

This paper addresses the problem of recognizing human skeletal interactions using multiview data captured from depth sensors. The interactions among people are important cues for group and crowd human behavior analysis. In this paper, we focus on modeling the person-person skeletal interactions for human activity recognition. First, we propose a novel graph model in each single-view case to encode class-specific person-person interaction patterns. Particularly, we model each person-person interaction by an attributed graph, which is designed to preserve the complex spatial structure among skeletal joints according to their activity levels as well as the spatio-temporal joint features. Then, combining the graph models for each single-view case, we propose the multigraph model to characterize each multiview interaction. Finally, we apply a general multiple kernel learning method to determine the optimal kernel weights for the proposed multigraph model while the optimal classifier is jointly learned. We evaluate the proposed approach on the M²I dataset, the SBU Kinect interaction dataset, and our interaction dataset. The experimental results show that our proposed approach outperforms several existing interaction recognition methods.