夜间无人机追踪难？新模型用渐进生成提升低分辨率目标清晰度。
DaDiff: Domain-aware Diffusion Model for Nighttime UAV Tracking
- 提出渐进式对齐框架，通过扩散模型逐步增强夜间低分辨率目标细节。
- 在自建的100序列夜间追踪数据集NUT-LR上实现更优跟踪精度与特征对齐。
- 适合做低光照、远距离视觉追踪的研究者，尤其关注目标细节恢复场景。
域自适应是解决昼夜图像特征不一致问题的有效方案，但传统单步适配方法难以应对夜间无人机视角下低分辨率（LR）目标因边缘模糊和细节缺失带来的挑战，且易受夜间噪声干扰。为此，本文提出一种新型渐进对齐范式——领域感知扩散模型（DaDiff），通过稳定生成过程将夜间低分辨率目标特征逐步对齐至白天特征。该模型包含：用于增强夜间低分辨率目标细节的对齐编码器；面向追踪任务设计的协同层；以及逐阶段区分不同扩散时间步特征分布的连续判别器。此外，构建了专为低分辨率目标设计的夜间无人机追踪基准NUT-LR，包含100个标注序列。大量实验验证了所提方法在鲁棒性与特征对齐能力上的优越性。源码与视频演示见https://github.com/vision4robotics/DaDiff。
原文摘要 · Abstract (English)
Domain adaptation is an inspiring solution to the misalignment issue of day/night image features for nighttime UAV tracking. However, the one-step adaptation paradigm is inadequate in addressing the prevalent difficulties posed by low-resolution (LR) objects when viewed from the UAVs at night, owing to the blurry edge contour and limited detail information. Moreover, these approaches struggle to perceive LR objects disturbed by nighttime noise. To address these challenges, this work proposes a novel progressive alignment paradigm, named domain-aware diffusion model (DaDiff), aligning nighttime LR object features to the daytime by virtue of progressive and stable generations. The proposed DaDiff includes an alignment encoder to enhance the detail information of nighttime LR objects, a tracking-oriented layer designed to achieve close collaboration with tracking tasks, and a successive distribution discriminator presented to distinguish different feature distributions at each diffusion timestep successively. Furthermore, an elaborate nighttime UAV tracking benchmark is constructed for LR objects, namely NUT-LR, consisting of 100 annotated sequences. Exhaustive experiments have demonstrated the robustness and feature alignment ability of the proposed DaDiff. The source code and video demo are available at https://github.com/vision4robotics/DaDiff.
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