Probabilistic Vision-Based Opponent Tracking in Robot Soccer

Good soccer players must keep their eyes on their opponents in order to make the right plays and moves. The same holds for soccer robots, too. In this paper, we apply probabilistic multiple object tracking to the continual estimation of the positions of opponent players in autonomous robot soccer. We extend MHT [3], an existing tracking algorithm, to handle multiple mobile sensors with uncertain positions, discuss the specification of probabilistic models needed by the algorithm, and describe the required vision-interpretation algorithms. The tracking algorithm enables robots to estimate the positions and motions of fast moving robots both accurately and robustly. We have applied the multiple object tracking algorithm throughout the RoboCup 2001 world championship. Empirical results show the applicability of multiple hypotheses tracking to vision-based opponent tracking and demonstrates the advantages for crowded environments.