Target-aware pooling combining global contexts for aerial tracking.

The UAVs captured targets are relatively small when compared with the ordinary surveillance cameras. Thus, a strong discriminative ability is required for aerial trackers to accurately locate small targets, especially in challenging scenes, including similar objects, occlusion and size change. Since most existing aerial trackers do not perform satisfactorily, in this paper, we design a tracker called TAP-GC that utilizes a weight-sharing deep CNN network to extract the multi-scale template and test features. We then construct a target-aware pooling module in the template branch, allowing the tracker to pay more attention to the target-related information. Thereafter, we directly fuse the template and test features through a transformer which is able to make full use of the global context, enabling the tracker to discriminate the target more accurately. Extensive experiments on well-known aerial tracking benchmarks, UAV123, UAV123@10fps and DTB70, show that our tracker outperforms a number of state-of-the-art trackers. In addition, when evaluating TAP-GC on OTB100, a tracking benchmark captured by ordinary cameras, it also achieves leading tracking performance. TAP-GC can achieve about 70 fps speed for real-time UAV tracking. [ABSTRACT FROM AUTHOR]