# ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking

arXiv: https://arxiv.org/abs/2106.02495 · HTML: https://mlsanity.com/paper/2106.02495/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [ECO: Efficient Convolution Operators for Tracking](https://mlsanity.com/paper/1611.09224/) (2016)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [Unsupervised Deep Tracking](https://mlsanity.com/paper/1904.01828/) (2019)
- [Learning Spatially Regularized Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1608.05571/) (2016)
- [Learning Background-Aware Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1703.04590/) (2017)
- [Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking](https://mlsanity.com/paper/1803.08679/) (2018)
- [AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization](https://mlsanity.com/paper/2003.12949/) (2020)
- [Staple: Complementary Learners for Real-Time Tracking](https://mlsanity.com/paper/1512.01355/) (2015)
- [Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking](https://mlsanity.com/paper/1908.02231/) (2019)
- [Discriminative Scale Space Tracking](https://mlsanity.com/paper/1609.06141/) (2016)
- [Discriminative Correlation Filter with Channel and Spatial Reliability](https://mlsanity.com/paper/1611.08461/) (2016)
- [Target-Aware Deep Tracking](https://mlsanity.com/paper/1904.01772/) (2019)
- [Kernel Cross-Correlator](https://mlsanity.com/paper/1709.05936/) (2017)
- [Integrating Boundary and Center Correlation Filters for Visual Tracking with Aspect Ratio Variation](https://mlsanity.com/paper/1710.02039/) (2017)
- [Automatic Failure Recovery and Re-Initialization for Online UAV Tracking with Joint Scale and Aspect Ratio Optimization](https://mlsanity.com/paper/2008.03915/) (2020)

## Built on by (most cited first, 17 of 17)

- [Unsupervised Domain Adaptation for Nighttime Aerial Tracking](https://mlsanity.com/paper/2203.10541/) (2022)
- [Towards Real-World Visual Tracking with Temporal Contexts](https://mlsanity.com/paper/2308.10330/) (2023)
- [DarkLighter: Light Up the Darkness for UAV Tracking](https://mlsanity.com/paper/2107.14389/) (2021)
- [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)
- [Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis](https://mlsanity.com/paper/2205.04281/) (2022)
- [VoxDet: Voxel Learning for Novel Instance Detection](https://mlsanity.com/paper/2305.17220/) (2023)
- [PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework](https://mlsanity.com/paper/2211.11629/) (2022)
- [MambaTrack: Exploiting Dual-Enhancement for Night UAV Tracking](https://mlsanity.com/paper/2411.15761/) (2024)
- [NT-VOT211: A Large-Scale Benchmark for Night-time Visual Object Tracking](https://mlsanity.com/paper/2410.20421/) (2024)
- [DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking](https://mlsanity.com/paper/2505.00752/) (2025)
- [Low-Light Object Tracking: A Benchmark](https://mlsanity.com/paper/2408.11463/) (2024)
- [UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions](https://mlsanity.com/paper/2507.00648/) (2025)
- [DaDiff: Domain-aware Diffusion Model for Nighttime UAV Tracking](https://mlsanity.com/paper/2410.12270/) (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)
- [SFDATrack: Generalized Source-Free Domain Adaptive Tracking Under Adverse Weather Conditions](https://mlsanity.com/paper/2607.00369/) (2026)

## Abstract (arXiv)

Prior correlation filter (CF)-based tracking methods for unmanned aerial vehicles (UAVs) have virtually focused on tracking in the daytime. However, when the night falls, the trackers will encounter more harsh scenes, which can easily lead to tracking failure. In this regard, this work proposes a novel tracker with anti-dark function (ADTrack). The proposed method integrates an efficient and effective low-light image enhancer into a CF-based tracker. Besides, a target-aware mask is simultaneously generated by virtue of image illumination variation. The target-aware mask can be applied to jointly train a target-focused filter that assists the context filter for robust tracking. Specifically, ADTrack adopts dual regression, where the context filter and the target-focused filter restrict each other for dual filter learning. Exhaustive experiments are conducted on typical dark sceneries benchmark, consisting of 37 typical night sequences from authoritative benchmarks, i.e., UAVDark, and our newly constructed benchmark UAVDark70. The results have shown that ADTrack favorably outperforms other state-of-the-art trackers and achieves a real-time speed of 34 frames/s on a single CPU, greatly extending robust UAV tracking to night scenes.