Robust and Real-Time Visual Tracking Based on Complementary Learners

Correlation filter based tracking methods have achieved impressive performance in recent years, showing high efficiency and robustness to challenging situations which exhibit illumination variations and motion blur. However, how to reduce model drift phenomenon which is usually caused by object deformation, abrupt motion, heavy occlusion and out-of-view, is still an open problem. In this paper, we exploit the low dimensional complementary features and an adaptive online detector with the average peak-to-correlation energy to improve tracking accuracy and time efficiency. Specifically, we appropriately integrate several complementary features in the correlation filter based discriminative framework and combine with the global color histogram to further boost the overall performance. In addition, we adopt the average peak-to-correlation energy to determine whether to activate and update an online CUR filter for re-detecting the target. We conduct extensive experiments on challenging OTB-15 benchmark datasets, and experimental results demonstrate that the proposed method achieves promising results in terms of efficiency, accuracy and robustness while running at 46 FPS.