Real-time tracking of highly articulated structures in the presence of noisy measurements

This paper presents a novel approach for model-based real-time tracking of highly articulated structures such as humans. This approach is based on an algorithm which efficiently, propagates statistics of probability distributions through a kinematic chain to obtain maximum a posteriori estimates of the motion of the entire structure. This algorithm yields the least squares solution in linear time (in the number of components of the model) and can also be applied to non-Gaussian statistics using a simple but powerful trick. The resulting implementation runs in real-time on standard hardware without any pre-processing of the video data and can thus operate on live video. Results from experiments performed using this system are presented and discussed.