# ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking

arXiv: https://arxiv.org/abs/2310.10071 · HTML: https://mlsanity.com/paper/2310.10071/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 36 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 30 of 31)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks](https://mlsanity.com/paper/1812.11703/) (2018)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [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)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [Deeper and Wider Siamese Networks for Real-Time Visual Tracking](https://mlsanity.com/paper/1901.01660/) (2019)
- [LaSOT: A High-quality Large-scale Single Object Tracking Benchmark](https://mlsanity.com/paper/2009.03465/) (2020)
- [Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark](https://mlsanity.com/paper/2103.16746/) (2021)
- [SwinTrack: A Simple and Strong Baseline for Transformer Tracking](https://mlsanity.com/paper/2112.00995/) (2021)
- [Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking](https://mlsanity.com/paper/2203.05328/) (2022)
- [Transforming Model Prediction for Tracking](https://mlsanity.com/paper/2203.11192/) (2022)
- [Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition](https://mlsanity.com/paper/1903.06150/) (2019)
- [LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search](https://mlsanity.com/paper/2104.14545/) (2021)
- [Learn to Match: Automatic Matching Network Design for Visual Tracking](https://mlsanity.com/paper/2108.00803/) (2021)
- [Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks](https://mlsanity.com/paper/1809.03355/) (2018)
- [Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking](https://mlsanity.com/paper/2308.06904/) (2023)
- [Efficient Visual Tracking with Exemplar Transformers](https://mlsanity.com/paper/2112.09686/) (2021)
- [FEAR: Fast, Efficient, Accurate and Robust Visual Tracker](https://mlsanity.com/paper/2112.07957/) (2021)
- [Distilled Siamese Networks for Visual Tracking](https://mlsanity.com/paper/1907.10586/) (2019)
- [FOVEA: Foveated Image Magnification for Autonomous Navigation](https://mlsanity.com/paper/2108.12102/) (2021)
- [A Closer Look at Self-Supervised Lightweight Vision Transformers](https://mlsanity.com/paper/2205.14443/) (2022)
- [Learning Target-aware Representation for Visual Tracking via Informative Interactions](https://mlsanity.com/paper/2201.02526/) (2022)
- [SALISA: Saliency-based Input Sampling for Efficient Video Object Detection](https://mlsanity.com/paper/2204.02397/) (2022)

## Built on by (most cited first, 25 of 25)

- [Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking](https://mlsanity.com/paper/2504.09228/) (2025)
- [MUST: The First Dataset and Unified Framework for Multispectral UAV Single Object Tracking](https://mlsanity.com/paper/2503.17699/) (2025)
- [ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking](https://mlsanity.com/paper/2507.19875/) (2025)
- [Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for Vision-Language Tracking](https://mlsanity.com/paper/2311.17085/) (2023)
- [Improving Visual Object Tracking through Visual Prompting](https://mlsanity.com/paper/2409.18901/) (2024)
- [FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking](https://mlsanity.com/paper/2504.13604/) (2025)
- [Tracking Transforming Objects: A Benchmark](https://mlsanity.com/paper/2404.18143/) (2024)
- [LoReTrack: Efficient and Accurate Low-Resolution Transformer Tracking](https://mlsanity.com/paper/2405.17660/) (2024)
- [Low-Light Object Tracking: A Benchmark](https://mlsanity.com/paper/2408.11463/) (2024)
- [Camouflaged Object Tracking: A Benchmark](https://mlsanity.com/paper/2408.13877/) (2024)
- [FARTrack: Fast Autoregressive Visual Tracking with High Performance](https://mlsanity.com/paper/2602.03214/) (2026)
- [Learning Motion Blur Robust Vision Transformers for Real-Time UAV Tracking](https://mlsanity.com/paper/2407.05383/) (2024)
- [Learning an Adaptive and View-Invariant Vision Transformer for Real-Time UAV Tracking](https://mlsanity.com/paper/2412.20002/) (2024)
- [TrackingMiM: Efficient Mamba-in-Mamba Serialization for Real-time UAV Object Tracking](https://mlsanity.com/paper/2507.01535/) (2025)
- [MSITrack: A Challenging Benchmark for Multispectral Single Object Tracking](https://mlsanity.com/paper/2510.06619/) (2025)
- [UAUTrack: Towards Unified Multimodal Anti-UAV Visual Tracking](https://mlsanity.com/paper/2512.02668/) (2025)
- [How Far are Modern Trackers from UAV-Anti-UAV? A Million-Scale Benchmark and New Baseline](https://mlsanity.com/paper/2512.07385/) (2025)
- [SOI is the Root of All Evil: Quantifying and Breaking Similar Object Interference in Single Object Tracking](https://mlsanity.com/paper/2508.09524/) (2025)
- [GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2602.14771/) (2026)
- [MUOT_3M: A 3 Million Frame Multimodal Underwater Benchmark and the MUTrack Tracking Method](https://mlsanity.com/paper/2602.18006/) (2026)
- [STATrack: A Target-Aware Fully Spiking Neural Network for Efficient UAV Tracking](https://mlsanity.com/paper/2603.27493/) (2026)
- [SpecTrack: Spectral Prompt Guided Adaptive Experts for Multispectral Object Tracking](https://mlsanity.com/paper/2607.05988/) (2026)
- [Cross-Modal UAV Object Tracking: State-Aware Representation Learning and A Unified Benchmark](https://mlsanity.com/paper/2607.18768/) (2026)
- [Counterfactual Motion Reliability Learning for Robust UAV Tracking](https://mlsanity.com/paper/2607.23209/) (2026)
- [Tracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion](https://mlsanity.com/paper/2609.17427/) (2026)

## Abstract (arXiv)

Recently, the transformer has enabled the speed-oriented trackers to approach state-of-the-art (SOTA) performance with high-speed thanks to the smaller input size or the lighter feature extraction backbone, though they still substantially lag behind their corresponding performance-oriented versions. In this paper, we demonstrate that it is possible to narrow or even close this gap while achieving high tracking speed based on the smaller input size. To this end, we non-uniformly resize the cropped image to have a smaller input size while the resolution of the area where the target is more likely to appear is higher and vice versa. This enables us to solve the dilemma of attending to a larger visual field while retaining more raw information for the target despite a smaller input size. Our formulation for the non-uniform resizing can be efficiently solved through quadratic programming (QP) and naturally integrated into most of the crop-based local trackers. Comprehensive experiments on five challenging datasets based on two kinds of transformer trackers, ie, OSTrack and TransT, demonstrate consistent improvements over them. In particular, applying our method to the speed-oriented version of OSTrack even outperforms its performance-oriented counterpart by 0.6% AUC on TNL2K, while running 50% faster and saving over 55% MACs. Codes and models are available at https://github.com/Kou-99/ZoomTrack.