Variational Monte-Carlo Approach for Articulated Object Tracking

In this paper, we describe a novel variational Monte Carlo approach for modeling and tracking body parts of articulated objects. An articulated object (human target) is represented as a dynamic Markov network of the different constituent parts. The proposed approach combines local information of individual body parts and other spatial constraints influenced by neighboring parts. The movement of the relative parts of the articulated body is modeled with local information of displacements from the Markov network and the global information from other neighboring parts. We explore the effect of certain model parameters (including the number of parts tracked; number of Monte-Carlo cycles, etc.) on system accuracy and show that ourvariational Monte Carlo approach achieves better efficiency and effectiveness compared to other methods on a number of real-time video datasets containing single targets.