Simultaneous Tracking of Multiple Targets Using SIR/MCMC Particle Filters by Distributed Cameras and Laser Range Finders

We are conducting the research project named Robot Town Project. The aim of this research is to develop a distributed sensor system such as cameras, laser range finders, and IC tags, and its management system so that autonomous robots can work with humans in an ordinary environment for daily human life. This paper presents a sensor network system consisting of distributed cameras and laser range finders for multiple objects tracking. Sensory information from cameras is processed by the Level Set Method in real time and integrated with range data obtained by laser range finders in a probabilistic manner using novel SIR/MCMC combined particle filters. Though the conventional SIR particle filter is a popular technique for object tracking, it has been pointed out that there are some drawbacks in practical applications such as its low tracking performance for multiple targets due to the degeneracy problem. In this paper, the new combined particle filters consisting of a low-resolution MCMC particle filter and a high-resolution SIR particle filter is proposed. Simultaneous tracking experiments for multiple moving targets are successfully carried out and it is verified that the combined particle filters has higher performance than the conventional particle filters in terms of the number of particles, the processing speed, and the tracking performance for multiple targets.