Robust and efficient UAV tracking via adaptive depth gating and prompt-guided autoregressive decoding

Abstract

Striking an optimal balance between robust tracking performance and real-time computational efficiency remains a significant challenge in the field of Unmanned Aerial Vehicle (UAV) tracking. UAV operational environments are characterized by rapid viewpoint changes, severe occlusions, and stringent onboard energy constraints, which impose rigorous demands on tracking algorithms. While recent generative trackers formulated as sequence interpretation tasks have achieved remarkable accuracy, their heavy autoregressive computations often result in high latency. Conversely, lightweight discriminative trackers frequently lack the temporal context required to handle long-term occlusions. To bridge this gap, we propose DAR-Track, a novel framework that harmonizes dynamic computation with generative modeling. First, we introduce an Adaptive Depth Gating (ADG) strategy to optimize the backbone encoder. This mechanism acts as an intelligent regulator, dynamically quantifying feature redundancy via inter-layer Euclidean distances and selectively bypassing saturated Transformer layers. Second, instead of traditional regression heads, we design a Prompt-Guided Autoregressive Decoder (PGAD). This module leverages historical coordinate sequences as spatio-temporal prompts to guide the generation of current target tokens. Furthermore, we employ a decoupled parallel attention mechanism to accelerate the decoding process. Extensive experiments on four authoritative benchmarks demonstrate that DAR-Track outperforms state-of-the-art methods, including MixFormer and SGLATrack, while maintaining superior inference speeds suitable for real-time aerial robotics.