TATrack: Target-oriented adaptive vision transformer for UAV tracking.

Abstract

Unmanned Aerial Vehicle (UAV) tracking requires accurate target localization from aerial top-down perspectives while operating under the computational constraints of aerial platforms. Current mainstream UAV trackers, constrained by the limited resources, predominantly employ lightweight Convolutional Neural Network (CNN) extractor, coupled with an appearance-based fusion mechanism. The absence of comprehensive target perception significantly constrains the balance between tracking accuracy and computational efficiency. To address this, we propose a target-oriented adaptive vision transformer for UAV tracking, named TATrack. TATrack utilizes a novel efficient transformer model, TA-ViT, to perform joint feature modeling and interaction under the orientation of the target. Specifically, TA-ViT employs an adaptive scoring suspension mechanism, wherein redundant network layers are bypassed when all token scores meet the suspension criteria, thereby enhancing inference speed. Moreover, positional information is utilized as a spatial-temporal prompt to enhance appearance-matching quality over time. By introducing location priors, we strengthen the visual perception of the target, which improves the target orientation and temporal continuity of the predicted position. Extensive experiments conducted across five UAV tracking benchmarks demonstrate that our method achieves an optimal balance between computational efficiency and tracking accuracy. The code will be available publicly.