3D human pose tracking using Gaussian process regression and particle filter applied to gait analysis of Parkinson's disease patients

Abstract

In this paper, we present a method to combine a Gaussian Process regression and a particle filter to track the 3D human pose in video sequences. We first build the probabilistic discriminative model that maps the silhouette descriptor to multiple 3D human poses using a Gaussian Process regression. The multimodal output distribution from the Gaussian Process regression are combined with the particle filter to track the 3D human pose in each frame of the video sequence. The predictions from the discriminative model are used to generate the hypothesis space for the particle filter and to initialize the tracking. We evaluate our approach on the HumanEva-I dataset and on the video sequences of Parkinson's patients. The evaluation results show that our approach does not require initialization and successfully tracks the 3D human pose over long video sequences.