# MambaTrack: Exploiting Dual-Enhancement for Night UAV Tracking

arXiv: https://arxiv.org/abs/2411.15761 · HTML: https://mlsanity.com/paper/2411.15761/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 33 of its 42 references that are in the map.
- First posted on arXiv 24 Nov 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 30 of 33)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline](https://mlsanity.com/paper/2204.04120/) (2022)
- [Pan-Mamba: Effective pan-sharpening with State Space Model](https://mlsanity.com/paper/2402.12192/) (2024)
- [HiFT: Hierarchical Feature Transformer for Aerial Tracking](https://mlsanity.com/paper/2108.00202/) (2021)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [Joint Visual Grounding and Tracking with Natural Language Specification](https://mlsanity.com/paper/2303.12027/) (2023)
- [Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement](https://mlsanity.com/paper/2405.03349/) (2024)
- [MixFormerV2: Efficient Fully Transformer Tracking](https://mlsanity.com/paper/2305.15896/) (2023)
- [Unsupervised Domain Adaptation for Nighttime Aerial Tracking](https://mlsanity.com/paper/2203.10541/) (2022)
- [Divert More Attention to Vision-Language Tracking](https://mlsanity.com/paper/2207.01076/) (2022)
- [CiteTracker: Correlating Image and Text for Visual Tracking](https://mlsanity.com/paper/2308.11322/) (2023)
- [All-Day Object Tracking for Unmanned Aerial Vehicle](https://mlsanity.com/paper/2101.08446/) (2021)
- [All in One: Exploring Unified Vision-Language Tracking with Multi-Modal Alignment](https://mlsanity.com/paper/2307.03373/) (2023)
- [WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking](https://mlsanity.com/paper/2201.07425/) (2022)
- [DarkLighter: Light Up the Darkness for UAV Tracking](https://mlsanity.com/paper/2107.14389/) (2021)
- [Tracker Meets Night: A Transformer Enhancer for UAV Tracking](https://mlsanity.com/paper/2303.10951/) (2023)
- [Mamba-FETrack: Frame-Event Tracking via State Space Model](https://mlsanity.com/paper/2404.18174/) (2024)

## Built on by (most cited first, 5 of 5)

- [COST: Contrastive One-Stage Transformer for Vision-Language Small Object Tracking](https://mlsanity.com/paper/2504.01321/) (2025)
- [Omni Survey for Multimodality Analysis in Visual Object Tracking](https://mlsanity.com/paper/2508.13000/) (2025)
- [How Far are Modern Trackers from UAV-Anti-UAV? A Million-Scale Benchmark and New Baseline](https://mlsanity.com/paper/2512.07385/) (2025)
- [Semantic-Aware Temporal Adaptation for UAV Anti-UAV Tracking](https://mlsanity.com/paper/2607.26511/) (2026)
- [DPTrack:Directional Kernel-Guided Prompt Learning for Robust Nighttime Aerial Tracking](https://mlsanity.com/paper/2510.15449/) (2025)

## Abstract (arXiv)

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× faster than CiteTracker and reduces 50.2% GPU memory. Our codes are available at urlhttps://github.com/983632847/Awesome-Multimodal-Object-Tracking.