Particle filter based object tracking in a stereo vision system

Tracking objects based on their visual features is of major importance for robotic systems performing autonomous tasks but also poses many challenges. This paper presents and compares two methods for tracking objects in a stereo camera system using particle filters which differ in the way they address the problem of stereo correspondence during the filtering process. In the first approach, two particle sets, one for each of the left and right stereo image frames are maintained and a mapping between the two sets is established by soft-stereo method, the particles are tracked in three-dimensional space and mapped back into the image frames to make the observations. The effectiveness of the approaches is demonstrated through experiments tracking a moving object and the two approaches are compared in terms of their convergence times and tracking errors