Articulated tracking with manifold regularized particle filter

Abstract

In this paper, we investigate articulated human motion tracking from video sequences using Bayesian approach. We derive a generic particle-based filtering procedure with a low-dimensional manifold. The manifold can be treated as a regularizer that enforces a distribution over poses during tracking process to be concentrated around the low-dimensional embedding. We refer to our method as manifold regularized particle filter. We present a particular implementation of our method based on back-constrained gaussian process latent variable model and gaussian diffusion. The proposed approach is evaluated using the real-life benchmark dataset HumanEva. We show empirically that the presented sampling scheme outperforms sampling-importance resampling and annealed particle filter procedures.