Tracking of multiple interacting objects using a novel prediction model
Tracking multiple interacting objects is an interesting and difficult task in computer vision. Two common problems in this field are a single object with multiple tracks and a single track with multiple objects. Most of the existing algorithms address the first problem but not the second one. In this paper, to solve the second problem we propose a new algorithm with a novel prediction model, which exploits the idea of penalizing outliers in statistics. The experiments show that our proposed algorithm is more robust than the existing algorithms in tackling both the aforementioned problems.