Adaptive Model Update Strategy for Correlation Filter Trackers

Abstract

Model update is an important module in target trackers. It plays an important role in adaptive tracking. Many researches have proven that different model update strategies should be adopted, when tracking in different scenes, especially in occlusion and deformation. Though many strategies have been proposed in recent years, few of them make high improvement and good combination on trackers. In this paper, we first proved there is a close relationship between the tracking scenes and the response maps. Then, we proposed an adaptive model update strategy for calculating model update rate based on the response map. Many experiments have been done to compare the proposed model update strategy with some state-of-the-art strategies, and the results have shown that the proposed model update strategy outperforms the best model update strategy by 7% on the test of Kernel Correlation Filter tracker. Furthermore, the proposed model update strategy was evaluated on some state-of-the-art correlation filter trackers. Results have shown the proposed strategy was well integrated into many trackers, and improved the tracking accuracy effectively.

Keywords

• Visual tracking
• model update
• correlation filter tracker
• image processing