The development of a real-time and robust RGB-T tracker is an extremely challenging task because the tracked object may suffer from shared and specific challenges in RGB and thermal (T) modalities. In this work, we observe that the implicit attribute information can boost the model discriminability, and propose a novel attribute-driven representation network to improve the RGB-T tracking performance. First, according to appearance change in RGB-T tracking scenarios, we divide the major and special challenges into four typical attributes: extreme illumination, occlusion, motion blur, and thermal crossover. Second, we design an attribute-driven residual branch for each heterogeneous attribute to mine the attribute-specific property and therefore build a powerful residual representation for object modeling. Furthermore, we aggregate these representations in channel and pixel levels by using the proposed attribute ensemble network (AENet) to adaptively fit the attribute-agnostic tracking process. The AENet can effectively make aware of appearance change while suppressing the distractors. Finally, we conduct numerous experiments on three RGB-T tracking benchmarks to compare the proposed trackers with other state-of-the-art methods. Experimental results show that our tracker achieves very competitive results with a real-time tracking speed. Code will be available at https://github.com/zhang-pengyu/ADRNet.

实时且稳健的 RGB-T 跟踪器的开发是一项极具挑战性的任务，因为被跟踪对象可能会遇到 RGB 和热 (T) 模式中的共享和特定挑战。在这项工作中，我们观察到隐式属性信息可以提高模型的可辨别性，并提出了一种新的属性驱动表示网络来提高 RGB-T 跟踪性能。首先，根据 RGB-T 跟踪场景中的外观变化，我们将主要和特殊挑战分为四个典型属性：极端光照、遮挡、运动模糊和热交叉。其次，我们为每个异构属性设计了一个属性驱动的残差分支，以挖掘特定于属性的属性，从而为对象建模构建强大的残差表示。此外，我们通过使用提议的属性集成网络（AENet）在通道和像素级别聚合这些表示，以自适应地适应与属性无关的跟踪过程。AENet 可以在抑制干扰因素的同时有效地感知外观变化。最后，我们对三个 RGB-T 跟踪基准进行了大量实验，以将所提出的跟踪器与其他最先进的方法进行比较。实验结果表明，我们的跟踪器以实时跟踪速度取得了非常有竞争力的结果。代码将在 https://github.com/zhang-pengyu/ADRNet 上提供。我们对三个 RGB-T 跟踪基准进行了大量实验，以将所提出的跟踪器与其他最先进的方法进行比较。实验结果表明，我们的跟踪器以实时跟踪速度取得了非常有竞争力的结果。代码将在 https://github.com/zhang-pengyu/ADRNet 上提供。我们对三个 RGB-T 跟踪基准进行了大量实验，以将所提出的跟踪器与其他最先进的方法进行比较。实验结果表明，我们的跟踪器以实时跟踪速度取得了非常有竞争力的结果。代码将在 https://github.com/zhang-pengyu/ADRNet 上提供。