3D Human motion tracking by exemplar-based conditional particle filter

Abstract

3D human motion tracking has received increasing attention in recent years due to its broad applications. Among various 3D human motion tracking methods, the particle filter is regarded as one of the most effective algorithms. However, there are still several limitations of current particle filter approaches such as low prediction accuracy and sensitivity to discontinuous motion caused by low frame rate or sudden change of human motion velocity. Targeting such problems, this paper presents a full-body human motion tracking system by proposing exemplar-based conditional particle filter (EC-PF) for monocular camera. By introducing a conditional term with respect to exemplars and image data, dynamic model is approximated and used to predict current states of particles in prediction phase. In update phase, weights of particles are refined by matching images with projected human model using a set of features.This method retains advantages of classic particle filters while increases prediction accuracy by replacing the smooth motion model with exemplars-based dynamic model which constrains evolved particles within an area closer to true state. Therefore, tracking robustness to discontinuous motion is improved such as under conditions of sudden change in motion velocity or using low-frame rate cameras. To verify the effectiveness and efficiency of the proposed algorithm, a variety of datasets are selected for testing and the results are also compared with the state-of-the-art methods in this domain.