Collaborative tracking: Dynamically fusing short-term trackers and long-term detector

This paper addresses the problem of long-term tracking of unknown objects in a video stream given its location in the first frame and without any other information. It's very challenging because of the existence of several factors such as frame cuts, sudden appearance changes and long-lasting occlusions etc. We propose a novel collaborative tracking framework fusing short-term trackers and long-term object detector. The short-term trackers consist of a frame-to-frame tracker and a weakly supervised tracker which would be updated under the weakly supervised information and re-initialized by long-term detector while the trackers fail. Additionally, the short-term trackers would provide multiple instance samples on the object trajectory for training a long-term detector with the bag samples with P-N constraints. Comprehensive experiments and comparisons demonstrate that our approaches achieve better performance than the state-of-the-art methods.