Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
