Particle Filter-Based Predictive Tracking of Futsal Players from a Single Stationary Camera

In this paper we study the use of computer vision techniques for visual tracking of futsal players. In the sports field, player tracking is an important task, as it can provide an estimate of the position of the athlete in a given time and thus compute his/her trajectories. This information can be used by coaches and sport professionals on tactical and physical analyses. We use adaptive background subtraction and blob analysis to detect players, as well as particle filters to predict their positions and track them using data from a single stationary camera. Experimental results show that our approach is capable to track players and compute their trajectories over time with errors below 20 cm, thus demonstrating a high potential to be used in a wide range of futsal match analyses.