Appearance-based multiple hypothesis tracking: Application to soccer broadcast videos analysis

Soccer is a popular sport in the world with the growth of demand for automatically analyzing matches and tactics. Since players are the focus of attention in soccer videos and they manage the entire game, player tracking is fundamental to most soccer video analysis. An efficient implementation of the multiple hypothesis tracking algorithm by evaluating its usefulness in the context of soccer player tracking is introduced in this paper. In contrast to the inherent linear assumption of multiple hypothesis tracking (MHT), which ignores appearance cues and occlusions, our approach relies on an appearance-based MHT (AMHT) framework by incorporating particle swarm optimization (PSO) to account for appearances, nonlinear movements and occlusions. Experimental results demonstrate the efficiency and robustness of the algorithm.