# Siamese Anchor Proposal Network for High-Speed Aerial Tracking

arXiv: https://arxiv.org/abs/2012.10706 · HTML: https://mlsanity.com/paper/2012.10706/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2020.

## Built on by (most cited first, 22 of 22)

- [HiFT: Hierarchical Feature Transformer for Aerial Tracking](https://mlsanity.com/paper/2108.00202/) (2021)
- [TCTrack: Temporal Contexts for Aerial Tracking](https://mlsanity.com/paper/2203.01885/) (2022)
- [Unsupervised Domain Adaptation for Nighttime Aerial Tracking](https://mlsanity.com/paper/2203.10541/) (2022)
- [SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking](https://mlsanity.com/paper/2106.08816/) (2021)
- [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)
- [Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking](https://mlsanity.com/paper/2308.11450/) (2023)
- [PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework](https://mlsanity.com/paper/2211.11629/) (2022)
- [BACTrack: Building Appearance Collection for Aerial Tracking](https://mlsanity.com/paper/2312.06136/) (2023)
- [SiamTHN: Siamese Target Highlight Network for Visual Tracking](https://mlsanity.com/paper/2303.12304/) (2023)
- [Multi-step Temporal Modeling for UAV Tracking](https://mlsanity.com/paper/2403.04363/) (2024)
- [Mutual-Learning Knowledge Distillation for Nighttime UAV Tracking](https://mlsanity.com/paper/2312.07884/) (2023)
- [Siamese Object Tracking for Vision-Based UAM Approaching with Pairwise Scale-Channel Attention](https://mlsanity.com/paper/2211.14564/) (2022)
- [Learning an Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking](https://mlsanity.com/paper/2412.20002/) (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)
- [Search Multilayer Perceptron-Based Fusion for Efficient and Accurate Siamese Tracking](https://mlsanity.com/paper/2603.01706/) (2026)
- [Dual Prompt-Driven Feature Encoding for Nighttime UAV Tracking](https://mlsanity.com/paper/2603.19628/) (2026)
- [Dual-branch Distilled Transformer for Efficient Asymmetric UAV Tracking](https://mlsanity.com/paper/2605.28018/) (2026)

## Abstract (arXiv)

In the domain of visual tracking, most deep learning-based trackers highlight the accuracy but casting aside efficiency. Therefore, their real-world deployment on mobile platforms like the unmanned aerial vehicle (UAV) is impeded. In this work, a novel two-stage Siamese network-based method is proposed for aerial tracking, textiti.e., stage-1 for high-quality anchor proposal generation, stage-2 for refining the anchor proposal. Different from anchor-based methods with numerous pre-defined fixed-sized anchors, our no-prior method can 1) increase the robustness and generalization to different objects with various sizes, especially to small, occluded, and fast-moving objects, under complex scenarios in light of the adaptive anchor generation, 2) make calculation feasible due to the substantial decrease of anchor numbers. In addition, compared to anchor-free methods, our framework has better performance owing to refinement at stage-2. Comprehensive experiments on three benchmarks have proven the superior performance of our approach, with a speed of around 200 frames/s.