# AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization

arXiv: https://arxiv.org/abs/2003.12949 · HTML: https://mlsanity.com/paper/2003.12949/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2020.

## Built on by (most cited first, 30 of 62)

- [Learning Target Candidate Association to Keep Track of What Not to Track](https://mlsanity.com/paper/2103.16556/) (2021)
- [Transforming Model Prediction for Tracking](https://mlsanity.com/paper/2203.11192/) (2022)
- [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)
- [Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook](https://mlsanity.com/paper/2112.02838/) (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)
- [WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking](https://mlsanity.com/paper/2201.07425/) (2022)
- [Towards Real-World Visual Tracking with Temporal Contexts](https://mlsanity.com/paper/2308.10330/) (2023)
- [Similarity-Guided Layer-Adaptive Vision Transformer for UAV Tracking](https://mlsanity.com/paper/2503.06625/) (2025)
- [Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking](https://mlsanity.com/paper/2504.09228/) (2025)
- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking](https://mlsanity.com/paper/2303.04378/) (2023)
- [ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking](https://mlsanity.com/paper/2106.02495/) (2021)
- [SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation](https://mlsanity.com/paper/2307.01024/) (2023)
- [Siamese Transformer Pyramid Networks for Real-Time UAV Tracking](https://mlsanity.com/paper/2110.08822/) (2021)
- [Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis](https://mlsanity.com/paper/2205.04281/) (2022)
- [Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking](https://mlsanity.com/paper/2104.03114/) (2021)
- [A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking](https://mlsanity.com/paper/2105.02480/) (2021)
- [Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking](https://mlsanity.com/paper/2308.11450/) (2023)
- [Progressive Representation Learning for Real-Time UAV Tracking](https://mlsanity.com/paper/2409.16652/) (2024)
- [Rank-Based Filter Pruning for Real-Time UAV Tracking](https://mlsanity.com/paper/2207.01768/) (2022)
- [VoxDet: Voxel Learning for Novel Instance Detection](https://mlsanity.com/paper/2305.17220/) (2023)
- [Local Perception-Aware Transformer for Aerial Tracking](https://mlsanity.com/paper/2208.00662/) (2022)
- [Predictive Visual Tracking: A New Benchmark and Baseline Approach](https://mlsanity.com/paper/2103.04508/) (2021)
- [Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking](https://mlsanity.com/paper/2203.01516/) (2022)
- [PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework](https://mlsanity.com/paper/2211.11629/) (2022)
- [Learning Disentangled Representation with Mutual Information Maximization for Real-Time UAV Tracking](https://mlsanity.com/paper/2308.10262/) (2023)
- [GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences](https://mlsanity.com/paper/2207.07629/) (2022)

## Abstract (arXiv)

Most existing trackers based on discriminative correlation filters (DCF) try to introduce predefined regularization term to improve the learning of target objects, e.g., by suppressing background learning or by restricting change rate of correlation filters. However, predefined parameters introduce much effort in tuning them and they still fail to adapt to new situations that the designer did not think of. In this work, a novel approach is proposed to online automatically and adaptively learn spatio-temporal regularization term. Spatially local response map variation is introduced as spatial regularization to make DCF focus on the learning of trust-worthy parts of the object, and global response map variation determines the updating rate of the filter. Extensive experiments on four UAV benchmarks have proven the superiority of our method compared to the state-of-the-art CPU- and GPU-based trackers, with a speed of ~60 frames per second running on a single CPU. Our tracker is additionally proposed to be applied in UAV localization. Considerable tests in the indoor practical scenarios have proven the effectiveness and versatility of our localization method. The code is available at https://github.com/vision4robotics/AutoTrack.