Robust global tracker based on an online estimation of tracklet descriptor reliability

The complex scene conditions such as light change, high density of mobile objects or object occlusion can cause object mis-detections. When a tracker can not recover these mis-detections, the trajectory of an object is fragmented into some short trajectories called tracklets. As a result, tracking quality is reduced remarkably. In this paper, we propose a new approach to improve the tracking quality by a global tracker which merges all tracklets belonging to an object in the whole video. Particularly, we compute descriptor reliability over time based on their discrimination. On the other hand, a motion model is also combined with appearance descriptors in a flexible way to improve the tracking quality. The proposed approach is evaluated on four benchmark datasets. The obtained results show the robustness and effectiveness of our approach compared to tracking as well as tracklet linking approaches from state of the art.