A robust likelihood function for 3D human pose tracking

Recent works on 3D human pose tracking using unsupervised methods typically focus on improving the optimization framework to find a better maximum in the likelihood function (i.e., the tracker). In contrast, in this paper, we focus on improving the likelihood function, by making it more robust and less ambiguous, thus making the optimization task easier. In particular, we propose an exponential chamfer distance for model matching that is robust to small pose changes, and a part-based model that is better able to localize partially occluded and overlapping parts. Using a standard annealing particle filter and simple diffusion motion model, the proposed likelihood function obtains significantly lower error than other unsupervised tracking methods on the HumanEva dataset. Noting that the joint system of the tracker's body model is different than the joint system of the motion capture ground-truth model, we propose a novel method for transforming between the two joint systems. Applying this bias correction, our part-based likelihood obtains results equivalent to state-of-the-art supervised tracking methods.