A3Track: Achieving Precise Target Tracking in Aerial Images With Receptive Field Alignment
Lei Xu, Chang Xu, Wensheng Cheng, Wen Yang, Gui-Song Xia
Abstract
Tracking arbitrary objects in aerial images presents formidable challenges to existing trackers. Among these challenges, the large scale variation and arbitrary geometry shape of visual targets are pronounced, resulting in two-fold mismatch issues between the feature receptive field and the tracking target. For one, there is a mismatch between the prior receptive field center and arbitrary-shaped targets. For another, the single receptive field mismatches the significantly scale-varied targets in the aerial imagery. To handle these challenges, we propose to Achieve precise Aerial tracking with receptive field Alignment, dubbed A3Track. The proposed A3Track is comprised of two modules: a Receptive Field Alignment (RFA) module and a Pyramid Receptive Field (PRF) module. First of all, we transform and update the receptive field center progressively, which drives the feature sampling location onto the targets’ main body, thus gradually yielding precise feature representation for arbitrary-shaped targets. We term this progressively updating process as the Receptive Field Alignment. Moreover, the PRF module constructs a set of pyramid features for the target, providing a multi-scale receptive field to handle the large scale variation of tracking objects. On four benchmarks, the new tracker A3Track achieves leading performance compared with existing methods and shows consistent improvements over baselines. The project is available at: https://chnleixu.github.io/A3Track-web/.