Visual Tracking Based on Multi-Feature and Fast Scale Adaptive Kernelized Correlation Filter

Abstract

Tracking methods based on a correlation filter have attracted much attention because of their high efficiency and strong robustness. However, a tracker based on a single feature is obviously not sufficient to adapt to the complex appearance changes of the target. Besides, rapid and exact scale estimation is still a challenging problem in the field of visual tracking. In this paper, we introduce an independent scale filter for the estimation of the scale of an object and merge two complementary features to further boost the performance of the tracker. At the same time, a dimension reduction strategy is adopted to decrease the computational load. Finally, a dynamic learning rate-based model update mechanism is inserted to effectively alleviate model degradation problem by suppressing the influence of noisy appearance changes. The extensive experiments were conducted on the object tracking benchmark (OTB) dataset and Temple color 128 dataset. The quantitative and qualitative results exhibit that compared with other popular trackers, the tracker proposed in this paper acquires favorable results in tracking accuracy, efficiency, and robustness. On the OTB-2015 benchmark dataset, it obtains precision scores of 0.773, 0.782, and 0.714 and success scores of 0.585, 0.606, and 0.534 in the three indexes of OPE, TRE, and SRE. On the Temple color 128 dataset, it acquires precision scores of 0.641, 0.681, and 0.606 and success scores of 0.478, 0.515, and 0.445 in the three indexes of OPE, TRE, and SRE, surpassing many well-known tracking methods. In terms of tracking efficiency, it runs at a speed of 42.3 frames/s on a single CPU, making it suitable for real-time applications.