# MambaTrack: Exploiting Dual-Enhancement for Night UAV Tracking Source: [MambaTrack: Exploiting Dual-Enhancement for Night UAV Tracking]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:57:26.101Z Authors: Chunhui Zhang, Li Liu, Hao Wen, Xi Zhou, Yanfeng Wang Published: 2024-11-24T09:12:37.000Z ## Abstract Night unmanned aerial vehicle (UAV) tracking is impeded by the challenges of poor illumination, with previous daylight-optimized methods demonstrating suboptimal performance in low-light conditions, limiting the utility of UAV applications. To this end, we propose an efficient mamba-based tracker, leveraging dual enhancement techniques to boost night UAV tracking. The mamba-based low-light enhancer, equipped with an illumination estimator and a damage restorer, achieves global image enhancement while preserving the details and structure of low-light images. Additionally, we advance a cross-modal mamba network to achieve efficient interactive learning between vision and language modalities. Extensive experiments showcase that our method achieves advanced performance and exhibits significantly improved computation and memory efficiency. For instance, our method is 2.8$\\times$ faster than CiteTracker and reduces 50.2$\\%$ GPU memory. Our codes are available at \\url{https://github.com/983632847/Awesome-Multimodal-Object-Tracking}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Anti-UAV Tracking on Anti-UAV318 (test)](): Anti-UAV Tracking; Anti-UAV318 (test); AUC: 61.9. - [Anti-UAV Tracking on DUT Anti-UAV (test)](): Anti-UAV Tracking; DUT Anti-UAV (test); AUC: 0.581. ## Original sources and implementation [Original paper]() [PDF]()