A Large-scale Sports Tracking Dataset and Progressive Re-detection Based Sports Tracking

Recent years have witnessed the great progress of Visual Object Tracking (VOT) which aims to predict the position of an object in each video frame given only its initial appearance. However, even the state-of-the-art methods are confronted with performance degradation, i.e., the tracker drift problem, in sports video scenes (e.g., soccer, basketball). There are two main causes that should be responsible for the tracker drift problem. First, the object of interest is often occluded by other objects that share a similar appearance. Such severe occlusion prevents the model from distinguishing the correct tracking object from other distractors in the future frames. Second, in sports videos, the objects often move fast from one place to another, which incurs severe blurry visual effects among consecutive frames. To address the issues of the tracker drift problem, we treat VOT as a tracking-by-re-detection task. Specifically, we detect candidate objects within a searching area (determined by object location in the previous frame) in the current frame and develop a progressive algorithm to filter out distractors in the area, which proves robust towards occlusion scenarios and tracker drift problems. Combining the advantages of our settings, the proposed framework method is robust to motion blur and object occlusion issues and achieves state-of-the-art tracking results on our challenging dataset.