PAM-based flexible generative topic model for 3D interactive activity recognition

Abstract

Interactive activity recognition from the RGB videos still remains a challenge, therefore some existing approaches paid the attention to RGB-Depth video process to avoid problems relating to mutual occlusion and redundant human pose and to improve accuracy of skeleton extraction. From the single action to complex interaction activity, it is necessary an efficient model to describe the relationship of body components between multi-human objects. In this research, the authors proposed a hierarchical model based on the Pachinko Allocation Model for interaction recognition. Concretely, the joint features comprising joint distant and joint motion are calculated from the skeleton position and then support to topic modeling. The probabilistic models describing the flexible relationship between features - poselets - activities are generated by this model. Finally, the Binary Tree of Support Vector Machine is applied for classification. Compared with existing state-of-the-arts, the proposed method outperforms in overall classification accuracy (8-21% approximately) with the SBU Kinect Interaction Dataset.