Human tracking using improved sample-based joint probabilistic data association filter

Abstract

The human tracking problem is a hot issue in human-robot interaction, in which a conventional algorithm sample-based joint probabilistic data association filters (SJPDAF) is widely used. In this paper, the algorithm is first extended to the situation of multi-sensor fusion and then accelerated to promote the real-time performance. The simulation and experiments on robots both show good results, reflecting the robust and the accuracy of our improved SJPDAF. © 2013 Springer-Verlag.