Graph-Based Sequential Particle Filtering Framework for Articulated Motion Analysis

Abstract

A general framework for sequential particle filtering on graphs is presented in this paper. We present two new articulated motion analysis and object tracking approaches: the graph-based sequential particle filtering framework for articulated object tracking and its hierarchical counterpart. Specifically, we estimate the interaction density by an efficient decomposed inter-part interaction model. To handle severe self-occlusion, we further formulate high-level inter-unit interaction and develop a hierarchical graph-based sequential particle filtering framework for articulated motion analysis. We rely on the proposed general framework of graph-based particle filtering for articulated motion analysis applications. The resulting experiments further demonstrate the superiority of our approach to tracking compared with existing methods.