A robust spatial-temporal correlation filter tracker for efficient UAV visual tracking

Correlation filters (CFs) have exhibited remarkable performance in visual target tracking, especially in aerial tracking of unmanned aerial vehicles (UAVs). Most existing CF-based trackers focus on how to effectively settle the unwanted boundary effect problem, while ignoring the different contributions of the discriminative features, which would lead to suboptimal performance in tracking. In this work, a robust spatial-temporal correlation filter, i.e., the temporal regularized background-aware correlation filter (TRBCF), is proposed. In detail, by extracting real background patches as negative samples and introducing a temporal regularization term, TRBCF improves the discriminability between the target and background in the spatial domain, and achieves continuous tracking in temporal sequences. Moreover, in order to selectively highlight the informative features and effectively represent the target, a novel multi-feature fusion mechanism based on the channel-wise response maps is proposed. Extensive experiments are conducted to evaluate the effectiveness of the proposed TRBCF on three classical UAV datasets (UAV123@10fps, DTB70, and UAVDT), and TRBCF performs favorably compared with the state-of-the-art trackers, with a real-time speed (41.38 fps) on a single CPU.