Particle Filter with Analytical Inference for Human Body Tracking
This paper introduces a framework that integrates analytical inference into the particle-filtering scheme for human body tracking. The analytical inference is provided by body parts detection, and is used to update subsets of state parameters representing the human pose. This reduces the degree of randomness and decreases the required number of particles. This new technique is a significant improvement over the standard particle filtering with the advantages of performing automatic track initialization, recovering from tracking failures, and reducing the computational load.