Tracking Human Body Parts Using Particle Filters Constrained by Human Biomechanics

In this paper, a novel framework for visual tracking of human body parts is introduced. The presented approach demonstrates the feasibility of recovering human poses with data from a single uncalibrated camera using a limb tracking system based on a 2D articulated model. It is constrained only by biomechanical knowledge about human bipedal motion, instead on relying on constraints linked to a specific activity or camera view. These characteristics make our approach suitable for real visual surveillance applications. Experiments on HumanEva I & II datasets demonstrate the effectiveness of our method on tracking human lower body parts. Moreover, a detail comparison with current tracking methods is presented.