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In recent years, discriminative correlation filter (DCF)-based trackers have made considerable progress and drawn widespread attention in the unmanned aerial vehicle (UAV) tracking community. Most existing trackers collect historical information, e.g., training samples, previous filters, and response maps, to promote their discrimination and robustness. Under UAV-specific tracking challenges, e.g., fast motion and view change, variations of both the target and its environment in the new frame are unpredictable. Interfered by future unknown environments, trackers that trained with historical information may be confused by the new context, resulting in tracking failure. In this paper, we propose a novel future-aware correlation filter tracker, i.e., FACF. The proposed method aims at effectively utilizing context information in the new frame for better discriminative and robust abilities, which consists of two stages: future state awareness and future context awareness. In the former stage, an effective time series forecast method is employed to reason a coarse position of the target, which is the reference for obtaining a context patch in the new frame. In the latter stage, we firstly obtain the single context patch with an efficient target-aware method. Then, we train a filter with the future context information in order to perform robust tracking. Extensive experimental results obtained from three UAV benchmarks, i.e., UAV123_10fps, DTB70, and UAVTrack112, demonstrate the effectiveness and robustness of the proposed tracker. Our tracker has comparable performance with other state-of-the-art trackers while running at ∼49 FPS on a single CPU. [ABSTRACT FROM AUTHOR]

Due to the high efficiency of discriminative correlation filter (DCF), it has attracted widespread attention in the field of UAV object tracking. To handle the problem of filter degradation, many trackers usually introduce temporal regularization to enhance the discriminative power of the filter. However, these temporal regularization methods only utilize the limited information between two consecutive frames, which are susceptible interference by previous corrupted information. Besides, regularization terms with predefined hyperparameters can not well adapt to the variations of the target across sequent frames, which may cause the model degradation or drift. We propose a tracker based on DCF framework to fully exploit the information hidden in the historical response map, namely adaptive weighted response consistency-based DCF tracking. Specifically, carefully selected historical response maps with fixed weight distribution are introduced in training phase to increase the robustness of the filter. Further, we present a unified loss for jointly learning the filter and the weight distribution, which can be solved by the alternate convex search method. The joint loss guarantees that reliable response maps contribute more to filter learning, leading to a more discriminative and adaptive filter for tracking the target. Extensive experiments show that the proposed method achieves state-of-the-art results on two datasets. [ABSTRACT FROM AUTHOR]