论文标题
DR^2 Track：通过干扰物抑制动态回归，朝着无人机的实时视觉跟踪
DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression
论文作者
论文摘要
视觉跟踪通过无人机（UAV）产生了有希望的应用。在文献中，先进的判别相关滤波器（DCF）类型跟踪器通常会通过学习的回归器将前景与背景区分开，该回归器将隐式循环样品回归到固定目标标签中。但是，预定义和不变的回归目标导致鲁棒性低，对不确定的空中跟踪方案的适应性。在这项工作中，我们利用在检测阶段生成的响应图的局部最大点来自动找到电流干扰器。通过压抑回归者学习中的分心者的响应，我们可以动态和适应性地改变回归目标，以利用跟踪鲁棒性和适应性。在三个具有挑战性的无人机基准上进行的大量实验表明，我们的跟踪器的出色性能和非凡的速度（在便宜的CPU上〜50fps）。
Visual tracking has yielded promising applications with unmanned aerial vehicle (UAV). In literature, the advanced discriminative correlation filter (DCF) type trackers generally distinguish the foreground from the background with a learned regressor which regresses the implicit circulated samples into a fixed target label. However, the predefined and unchanged regression target results in low robustness and adaptivity to uncertain aerial tracking scenarios. In this work, we exploit the local maximum points of the response map generated in the detection phase to automatically locate current distractors. By repressing the response of distractors in the regressor learning, we can dynamically and adaptively alter our regression target to leverage the tracking robustness as well as adaptivity. Substantial experiments conducted on three challenging UAV benchmarks demonstrate both excellent performance and extraordinary speed (~50fps on a cheap CPU) of our tracker.
