Discriminative correlation filters (DCFs) have recently achieved competitive performance in visual tracking benchmarks. However, such trackers perform poorly when the target undergoes occlusion, viewpoint variation or other challenging attributes. To tackle these issues, in this Letter, the authors combine the fast DCF trackers with the precise deep learning methods to eliminate the accumulating drift for the vehicle tracking based on unmanned aerial vehicle platform. Specifically, the authors employ the tracking result of the DCF tracker as the input of the boundary regressing network. After judging the existence of the target in the input patch, the proposed network would estimate the boundary of the target vehicle. Furthermore, the output would be updated to the tracking template, aiming at eliminating the accumulation errors and achieving a long-term tracking. The effectiveness of the proposed algorithm is validated through experimental comparison on widely used tracking benchmark data sets.

中文翻译：

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基于无人机的边界感知车辆跟踪

判别相关滤波器 (DCF) 最近在视觉跟踪基准测试中取得了有竞争力的性能。然而，当目标受到遮挡、视点变化或其他具有挑战性的属性时，这种跟踪器的性能很差。为了解决这些问题，在这封信中，作者将快速 DCF 跟踪器与精确的深度学习方法相结合，以消除基于无人机平台的车辆跟踪的累积漂移。具体来说，作者采用 DCF 跟踪器的跟踪结果作为边界回归网络的输入。在判断输入补丁中目标的存在后，所提出的网络将估计目标车辆的边界。此外，输出将更新为跟踪模板，旨在消除累积误差，实现长期跟踪。通过对广泛使用的跟踪基准数据集的实验比较，验证了所提出算法的有效性。