With the wide vision and high flexibility, unmanned aerial vehicle (UAV) has been widely used into object tracking in recent years. However, its limited computing capability poses a great challenges to tracking algorithms. On the other hand, Discriminative Correlation Filter (DCF) based trackers have attracted great attention due to their computational efficiency and superior accuracy. Many studies introduce spatial and temporal regularization into the DCF framework to achieve a more robust appearance model and further enhance the tracking performance. However, such algorithms generally set fixed spatial or temporal regularization parameters, which lack flexibility and adaptability under cluttered and challenging scenarios. To tackle such issue, in this letter, we propose a novel DCF tracking model by introducing dynamic spatial regularization weight, which encourage the filter focuses on more reliable region during training stage. Furthermore, our method could optimize the spatial and temporal regularization weight simultaneously using Alternative Direction Method of Multiplies (ADMM) technique method, where each sub-problem has closed-form solution. Through the joint optimization, our tracker could not only suppress the potential distractors but also construct robust target appearance on the basis of reliable historical information. Experiments on two UAV benchmarks have demonstrated that our tracker performs favorably against other state-of-the-art algorithms.

中文翻译：

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学习无人机目标跟踪的动态时空正则化


无人机（UAV）凭借宽视野和高灵活性，近年来被广泛应用于目标跟踪。然而其有限的计算能力给跟踪算法带来了巨大的挑战。另一方面，基于判别相关滤波器（DCF）的跟踪器由于其计算效率和卓越的精度而引起了极大的关注。许多研究将空间和时间正则化引入DCF框架中，以实现更鲁棒的外观模型并进一步增强跟踪性能。然而，此类算法通常设置固定的空间或时间正则化参数，在杂乱和具有挑战性的场景下缺乏灵活性和适应性。为了解决这个问题，在这封信中，我们提出了一种新颖的 DCF 跟踪模型，通过引入动态空间正则化权重，鼓励滤波器在训练阶段关注更可靠的区域。此外，我们的方法可以使用替代方向乘法（ADMM）技术方法同时优化空间和时间正则化权重，其中每个子问题都有封闭式解。通过联合优化，我们的跟踪器不仅可以抑制潜在的干扰因素，还可以在可靠的历史信息的基础上构建稳健的目标外观。两个无人机基准测试的实验表明，我们的跟踪器的性能优于其他最先进的算法。