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.

中文翻译：

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学习自适应加权响应一致性相关滤波器用于实时无人机跟踪

由于判别相关滤波器（DCF）的高效率，它在无人机目标跟踪领域引起了广泛关注。为了处理过滤器退化的问题，许多跟踪器通常引入时间正则化来增强过滤器的判别能力。然而，这些时间正则化方法仅利用两个连续帧之间的有限信息，容易受到先前损坏信息的干扰。此外，具有预定义超参数的正则化项不能很好地适应目标跨连续帧的变化，这可能导致模型退化或漂移。我们提出了一种基于 DCF 框架的跟踪器，以充分利用隐藏在历史响应图中的信息，即基于自适应加权响应一致性的 DCF 跟踪。具体来说，在训练阶段引入了精心挑选的具有固定权重分布的历史响应图，以增加过滤器的鲁棒性。此外，我们提出了联合学习滤波器和权重分布的统一损失，这可以通过交替凸搜索方法解决。联合损失保证了可靠的响应图对过滤器学习的贡献更大，从而产生用于跟踪目标的更具辨别力和自适应性的过滤器。大量实验表明，所提出的方法在两个数据集上取得了最先进的结果。这可以通过交替凸搜索方法解决。联合损失保证了可靠的响应图对过滤器学习的贡献更大，从而产生用于跟踪目标的更具辨别力和自适应性的过滤器。大量实验表明，所提出的方法在两个数据集上取得了最先进的结果。这可以通过交替凸搜索方法解决。联合损失保证了可靠的响应图对过滤器学习的贡献更大，从而产生用于跟踪目标的更具辨别力和自适应性的过滤器。大量实验表明，所提出的方法在两个数据集上取得了最先进的结果。