NightTrack: Joint Night-Time Image Enhancement and Object Tracking for UAVs

Highlights: What are the main findings? We propose NightTrack, a unified framework that jointly optimizes low-light enhancement and object tracking, outperforming state-of-the-art methods in night-time UAV scenarios. By introducing Pyramid Attention Modules (PAMs) and jointly estimating illumination and noise, the framework significantly enhances the discriminability of features in low-light conditions. What is the implication of the main finding? The unified paradigm of integrating enhancement and tracking offers a more effective solution for tasks with competing objectives than traditional two-stage pipelines. The proposed framework substantially improves the robustness and precision of night-time UAV tracking, presenting a novel perspective to enable practical applications in challenging low-light environments. UAV-based visual object tracking has recently become a prominent research focus in computer vision. However, most existing trackers are primarily benchmarked under well-illuminated conditions, largely overlooking the challenges that may arise in night-time scenarios. Although attempts exist to restore image brightness via low-light image enhancement before feeding frames to a tracker, such two-stage pipelines often struggle to strike an effective balance between the competing objectives of enhancement and tracking. To address this limitation, this work proposes NightTrack, a unified framework that optimizes both low-light image enhancement and UAV object tracking. While boosting image visibility, NightTrack not only explicitly preserves but also reinforces the discriminative features required for robust tracking. To improve the discriminability of low-light representations, Pyramid Attention Modules (PAMs) are introduced to enhance multi-scale contextual cues. Moreover, by jointly estimating illumination and noise curves, NightTrack mitigates the potential adverse effects of low-light environments, leading to significant gains in precision and robustness. Experimental results on multiple night-time tracking benchmarks demonstrate that NightTrack outperforms state-of-the-art methods in night-time scenes, exhibiting strong promises for further development.