A novel kernelized correlation filter by fusing multiple feature response maps, enhanced target re-detection, and improved model updating for visual tracking

Visual target tracking remains a hard research problem due to many unpredictable challenges (e.g., complete occlusion, out-of-view, and fast motion). This paper proposes a novel kernelized correlation filter architecture based on the fusion of the multiple feature response maps (FRMs), enhanced target re-detection, and improved online model updating. First, the multi-feature fusion strategy reveals that complementary feature information paves the way for effectively improving the target recognition ability. Meanwhile, a novel target re-detection module performs superior in relocating the tracked target and mitigating the model drift problem. Moreover, most existing trackers may indiscriminately update the model when encountering inaccurate predictions, eventually induce undesired model drift and tracking performance decay. Inspiring by the average peak-to-correlation energy, we apply an improved model updating scheme to ascertain the relative fluctuated level of FRMs. This novel model updating method can reject deteriorated samples and improves the discriminative power of the model. As evaluated on the four popular tracking benchmark datasets (including OTB-2013, OTB-2015, UAV123, and LaSOT), benefiting from proposed methods, our tracker obtains better effectiveness and efficiency than that of 28 state-of-the-art competitors (including 17 correlation filter-based competitors and 11 deep learning-based competitors). Meanwhile, our proposed tracker can meet the requirements of limited storage space and real-time performance.