# Tracker Meets Night: A Transformer Enhancer for UAV Tracking

arXiv: https://arxiv.org/abs/2303.10951 · HTML: https://mlsanity.com/paper/2303.10951/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 38 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [Multi-Scale Progressive Fusion Network for Single Image Deraining](https://mlsanity.com/paper/2003.10985/) (2020)
- [Probabilistic Regression for Visual Tracking](https://mlsanity.com/paper/2003.12565/) (2020)
- [LocalViT: Analyzing Locality in Vision Transformers](https://mlsanity.com/paper/2104.05707/) (2021)
- [Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation](https://mlsanity.com/paper/2103.00860/) (2021)
- [Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation](https://mlsanity.com/paper/2005.14553/) (2020)
- [DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation](https://mlsanity.com/paper/2104.10834/) (2021)
- [HiFT: Hierarchical Feature Transformer for Aerial Tracking](https://mlsanity.com/paper/2108.00202/) (2021)
- [SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking](https://mlsanity.com/paper/2106.08816/) (2021)
- [All-Day Object Tracking for Unmanned Aerial Vehicle](https://mlsanity.com/paper/2101.08446/) (2021)
- [Recurrent Exposure Generation for Low-Light Face Detection](https://mlsanity.com/paper/2007.10963/) (2020)
- [DarkLighter: Light Up the Darkness for UAV Tracking](https://mlsanity.com/paper/2107.14389/) (2021)
- [On the Visual-based Safe Landing of UAVs in Populated Areas: a Crucial Aspect for Urban Deployment](https://mlsanity.com/paper/2102.13253/) (2021)

## Built on by (most cited first, 23 of 23)

- [SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation](https://mlsanity.com/paper/2307.01024/) (2023)
- [DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs](https://mlsanity.com/paper/2309.10491/) (2023)
- [RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning](https://mlsanity.com/paper/2409.13366/) (2024)
- [Prompt-Driven Temporal Domain Adaptation for Nighttime UAV Tracking](https://mlsanity.com/paper/2409.18533/) (2024)
- [MambaTrack: Exploiting Dual-Enhancement for Night UAV Tracking](https://mlsanity.com/paper/2411.15761/) (2024)
- [XS-VID: A Large-Scale Benchmark for Small Object Detection and Tracking in Videos](https://mlsanity.com/paper/2407.18137/) (2024)
- [NT-VOT211: A Large-Scale Benchmark for Night-time Visual Object Tracking](https://mlsanity.com/paper/2410.20421/) (2024)
- [Towards a General-Purpose Zero-Shot Synthetic Low-Light Image and Video Pipeline](https://mlsanity.com/paper/2504.12169/) (2025)
- [DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking](https://mlsanity.com/paper/2505.00752/) (2025)
- [Recent Advances in Transformer and Large Language Models for UAV Applications](https://mlsanity.com/paper/2508.11834/) (2025)
- [Mutual-Learning Knowledge Distillation for Nighttime UAV Tracking](https://mlsanity.com/paper/2312.07884/) (2023)
- [Low-Light Object Tracking: A Benchmark](https://mlsanity.com/paper/2408.11463/) (2024)
- [UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking](https://mlsanity.com/paper/2409.03245/) (2024)
- [Enhancing Nighttime UAV Tracking with Light Distribution Suppression](https://mlsanity.com/paper/2409.16631/) (2024)
- [MambaNUT: Nighttime UAV Tracking via Mamba-based Adaptive Curriculum Learning](https://mlsanity.com/paper/2412.00626/) (2024)
- [DaDiff: Domain-aware Diffusion Model for Nighttime UAV Tracking](https://mlsanity.com/paper/2410.12270/) (2024)
- [ELVIS: Enhance Low-Light for Video Instance Segmentation in the Dark](https://mlsanity.com/paper/2512.01495/) (2025)
- [Latent Disentanglement for Low Light Image Enhancement](https://mlsanity.com/paper/2408.06245/) (2024)
- [DPTrack:Directional Kernel-Guided Prompt Learning for Robust Nighttime Aerial Tracking](https://mlsanity.com/paper/2510.15449/) (2025)
- [MATrack: Efficient Multiscale Adaptive Tracker for Real-Time Nighttime UAV Operations](https://mlsanity.com/paper/2510.21586/) (2025)
- [Dual Prompt-Driven Feature Encoding for Nighttime UAV Tracking](https://mlsanity.com/paper/2603.19628/) (2026)
- [TAE: Target-aware enhancer for nighttime UAV tracking](https://mlsanity.com/paper/2605.29558/) (2026)
- [UniRef-UAV: A Multimodal Benchmark for Universal Referring in UAV Imagery](https://mlsanity.com/paper/2607.08267/) (2026)

## Abstract (arXiv)

Most previous progress in object tracking is realized in daytime scenes with favorable illumination. State-of-the-arts can hardly carry on their superiority at night so far, thereby considerably blocking the broadening of visual tracking-related unmanned aerial vehicle (UAV) applications. To realize reliable UAV tracking at night, a spatial-channel Transformer-based low-light enhancer (namely SCT), which is trained in a novel task-inspired manner, is proposed and plugged prior to tracking approaches. To achieve semantic-level low-light enhancement targeting the high-level task, the novel spatial-channel attention module is proposed to model global information while preserving local context. In the enhancement process, SCT denoises and illuminates nighttime images simultaneously through a robust non-linear curve projection. Moreover, to provide a comprehensive evaluation, we construct a challenging nighttime tracking benchmark, namely DarkTrack2021, which contains 110 challenging sequences with over 100 K frames in total. Evaluations on both the public UAVDark135 benchmark and the newly constructed DarkTrack2021 benchmark show that the task-inspired design enables SCT with significant performance gains for nighttime UAV tracking compared with other top-ranked low-light enhancers. Real-world tests on a typical UAV platform further verify the practicability of the proposed approach. The DarkTrack2021 benchmark and the code of the proposed approach are publicly available at https://github.com/vision4robotics/SCT.