Particle filter based human motion tracking

Abstract

This paper proposes a particle filter based marker-less upper body motion capture system, capable of running in realtime. This system is designed for a humanoid robot application, and thus a monocular image sequence is used as input. We first set up a model of the human body, a sub-model which includes 11 Degrees of Freedom is used for the upper body tracking. Considering the realtime processing requirements, two time efficient cues are implemented in the likelihood calculation, namely the edge cue and the distance cue. The system is tested using a publicly available database, which consists of both the videos and the ground truth data, enabling quantitative error analysis. The system successfully tracks the human through arbitrary upper body motion at 20Hz.