Coarse-to-Fine UAV Target Tracking With Deep Reinforcement Learning

Abstract

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.