Toward Real-Time UAV Tracking With Adaptive and Background-Aware Vision Transformers

In Unmanned Aerial Vehicle (UAV) tracking, discriminative correlation filters (DCF) are popular for their speed, making them ideal for real-time use with limited resources. Recently, lightweight convolutional neural networks (CNNs) have offered a new approach. Through filter pruning, these CNNs maintain high accuracy and efficiency, making them a strong alternative, especially for greater precision. Despite these advancements, the potential of pure vision transformers (ViTs) in UAV tracking remains largely untapped, especially based on the paradigm of conditional computation. In this work, we introduce an adaptive and background-aware Vision Transformer (Aba-ViT) and leverage it to develop a real-time UAV tracking framework called Aba-ViTrack. The proposed Aba-ViT exploits an adaptive and background-aware token computation method to reduce inference time. This approach adaptively discards tokens based on learned halting probabilities, which a priori are higher for background tokens than target ones. To further improve efficiency, this paper proposes a novel classroom-style learning (CSL) approach, where robustness knowledge is transmitted vertically from teacher to students, and generalization capability is enhanced through horizontal mutual learning among students. This method is used to compress Aba-ViTrack, resulting in Aba-ViTrack++. The upgraded version achieves a better balance between accuracy and efficiency in real-time UAV tracking. This version achieves a better balance between accuracy and efficiency for real-time UAV tracking. Extensive experiments on six UAV tracking benchmarks demonstrate that the proposed method achieves state-of-the-art performance in UAV tracking. The code is available at https://github.com/xyyang317/Aba-ViTrack.