NiDR: Nighttime Aerial Tracking via Decoupled Representations

Xu Lei, Yan Zhang, Chang Xu, Wensheng Cheng, Wen Yang

Abstract: Vanilla aerial trackers exhibit sensitivity to low-light conditions (e.g., nighttime aerial tracking scenario). To mitigate this, existing methods incorporate the light enhancement method as a preprocessing for aerial tracking. Despite the advancements, these approaches are restricted to the disparity in task objectives between the enhancer and tracker. Motivated by the observation that feature channels exhibit varying sensitivity to illumination, we propose to decouple the feature representation into two distinct parts: 1) illumination-invariant feature embedding and 2) illumination-sensitive feature embedding. The former, realized by the illumination invariant embedding (IIE) module, enhances features that remain invariant to illumination changes. Meanwhile, the latter, facilitated by the illumination sensitive embedding (ISE) module, aims to mitigate the negative impact of illumination-sensitive features on tracking performance. Building upon this decoupling strategy, we introduce NiDR, a simple yet effective nighttime aerial tracker. The proposed NiDR exhibits strong performance on three nighttime aerial tracking benchmarks (i.e., UAVDark135, NAT2021, and DarkTrack2021). Notably, it outperforms previous competitors by large margins, e.g., 3.1 points on the UAVDark135 and 2.0 points on the Darktrack2021 in terms of precision for nighttime scenarios.