Coarse-to-Fine UAV Target Tracking With Deep Reinforcement Learning
Coarse-to-Fine UAV Target Tracking With Deep Reinforcement Learning
复制标题
通过深度强化学习进行从粗到细的无人机目标跟踪
DOI：
10.1109/tase.2018.2877499
复制
发表时间：
2019-10-01
影响因子：
5.6
通讯作者：
Li, Yibin
中科院分区：
文献类型：
--
作者：
Zhang, Wei;Song, Ke;Li, Yibin
在无人机跟踪任务中，目标的纵横比经常变化，这使得空中跟踪非常具有挑战性。传统的跟踪器面临这样的问题，因为它们主要通过保持一定的纵横比来关注尺度变化问题。在本文中，我们提出了一种从粗到细的深度方案来解决无人机跟踪中的纵横比变化问题。粗跟踪器首先生成目标对象的初始估计，然后学习一系列动作来微调边界框的四个边界。粗跟踪器和细跟踪器被设计成具有不同的动作空间和操作目标。前者主导整个边界框，后者则侧重于每个边界的细化。他们通过共享感知网络和端到端强化学习架构来进行联合训练。基准航空数据集的实验结果证明，所提出的方法优于现有跟踪器，并在处理无人机跟踪中的纵横比变化方面产生显着的精度增益。从业者须知——在过去的几年里，无人机 (UAV) 在工业和消费用途方面都受到了广泛关注。迫切需要赋予无人机基于智能视觉的技术，而通过视觉跟踪方法自动进行目标跟踪作为最基本的智能功能之一，可以促进无人机的各种应用，例如监视、增强现实和行为建模。然而，基于无人机的跟踪方法的主要问题是平台本身：它不稳定，容易突然运动，产生非均匀数据（尺度、角度、旋转、深度等），所有这些都容易频繁改变目标的纵横比，进一步增加了目标跟踪的难度。本文旨在解决无人机跟踪中的宽高比变化（ARC）问题。我们提出了一种从粗到细的无人机跟踪策略。具体地，首先获得粗边界框来定位目标。然后，对每个边界执行细化方案以进一步改进位置估计。事实证明，跟踪器可以有效地增加对ARC的抵抗力。这种方法可以在无人机上实现，以提高目标跟踪性能。
The aspect ratio of a target changes frequently during an unmanned aerial vehicle (UAV) tracking task, which makes the aerial tracking very challenging. Traditional trackers struggle from such a problem as they mainly focus on the scale variation issue by maintaining a certain aspect ratio. In this paper, we propose a coarse-to-fine deep scheme to address the aspect ratio variation in UAV tracking. The coarse-tracker first produces an initial estimate for the target object, then a sequence of actions are learned to fine-tune the four boundaries of the bounding box. The coarse-tracker and the fine-tracker are designed to have different action spaces and operating target. The former dominates the entire bounding box and the latter focuses on the refinement of each boundary. They are trained jointly by sharing the perception network with an end-to-end reinforcement learning architecture. Experimental results on benchmark aerial data set prove that the proposed approach outperforms existing trackers and produces significant accuracy gains in dealing with the aspect ratio variation in UAV tracking. Note to Practitioners—During the past years, unmanned aerial vehicle (UAV) have gained much attention for both industrial and consumer uses. It is in urgent demand to endow the UAV with intelligent vision-based techniques, and the automatic target following via visual tracking methods as one of the most fundamental intelligent features could promote various applications of UAVs, such as surveillance, augmented reality, and behavior modeling. Nonetheless, the primary issue of a UAV-based tracking method is the platform itself: it is not stable, it tends to have sudden movements, it generates nonhomogeneous data (scale, angle, rotation, depth, and so on), all of them tend to change the aspect ratio of the target frequently and further increase the difficulty of object tracking. This paper aims to address the aspect ratio change (ARC) problem in UAV tracking. We present a coarse-to-fine strategy for UAV tracking. Specifically, the coarse bounding box is obtained to locate the target firstly. Then, a refinement scheme is performed on each boundary to further improve the position estimate. The tracker is proved to be effective to increase the resistance to the ARC. Such a method can be implemented on UAV to improve the target-following performance.