A Novel Incremental Multi-Template Update Strategy for Robust Object Tracking

In the field of correlation filter object tracking, the traditional template-update method easily causes template drift, so it performs poorly in complex scenes. To enhance the robustness of the template, a novel incremental multi-template update strategy is proposed in this paper. We find that reliability varies among all historical filters and that highly reliable filters are key to achieving accurate tracking. The incremental multi-template update strategy combines the local maximum-reliability filter template with the historical filter template incrementally, which is obviously different from the traditional update method. We apply this strategy to two trackers with superior performance. The experimental results of three test benchmarks, including the VOT2016, OTB100 and UAV123 datasets, show that the performance of our trackers is superior to that of the state-of-the-art trackers.