Abstract
This paper proposes and algorithm for extended object tracking using sparse feature points. The described technique is based on the Rao-Blackwellized Particle Filter. In particular, two different data association techniques that take into consideration clutter and missed detections, are coupled and tested in order to provide a comparison of their performance for the problem of extended object tracking.