Starts Better and Ends Better: A Target Adaptive Image Signature Tracker

Correlation filter (CF) trackers have achieved outstanding performance in visual object tracking tasks, in which the cosine mask plays an essential role in alleviating boundary effects caused by the circular assumption. However, the cosine mask imposes a larger weight on its center position, which greatly affects CF trackers, that is, their performance will drop significantly if a bad starting point happens to occur. To address the above issue, we propose a target adaptive image signature (TaiS) model to refine the starting point in each frame for CF trackers. Specifically, we incorporate the target priori into the image signature to build a target-specific saliency map, and iteratively refine the starting point with a closed-form solution during the tracking process. As a result, our TaiS is able to find a better starting point close to the center of targets; more importantly, it is independent of specific CF trackers and can efficiently improve their performance. Experiments on two benchmark datasets, i.e., OTB100 and UAV123, demonstrate that our TaiS consistently achieves high performance and updates the state of the arts in visual tracking. The source code of our approach will be made publicly available.