# AVisT: A Benchmark for Visual Object Tracking in Adverse Visibility

arXiv: https://arxiv.org/abs/2208.06888 · HTML: https://mlsanity.com/paper/2208.06888/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 14 Aug 2022.

## Builds on (most cited first, 22 of 22)

- [GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild](https://mlsanity.com/paper/1810.11981/) (2018)
- [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)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [Fast Online Object Tracking and Segmentation: A Unifying Approach](https://mlsanity.com/paper/1812.05050/) (2018)
- [Probabilistic Regression for Visual Tracking](https://mlsanity.com/paper/2003.12565/) (2020)
- [Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking](https://mlsanity.com/paper/2103.11681/) (2021)
- [Ocean: Object-aware Anchor-free Tracking](https://mlsanity.com/paper/2006.10721/) (2020)
- [Siamese Box Adaptive Network for Visual Tracking](https://mlsanity.com/paper/2003.06761/) (2020)
- [Need for Speed: A Benchmark for Higher Frame Rate Object Tracking](https://mlsanity.com/paper/1703.05884/) (2017)
- [Learning Target Candidate Association to Keep Track of What Not to Track](https://mlsanity.com/paper/2103.16556/) (2021)
- [Know Your Surroundings: Exploiting Scene Information for Object Tracking](https://mlsanity.com/paper/2003.11014/) (2020)
- [Transforming Model Prediction for Tracking](https://mlsanity.com/paper/2203.11192/) (2022)
- [A Novel Performance Evaluation Methodology for Single-Target Trackers](https://mlsanity.com/paper/1503.01313/) (2015)
- [Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation](https://mlsanity.com/paper/2007.02024/) (2020)
- [Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation](https://mlsanity.com/paper/2011.11630/) (2020)
- [Robust Visual Tracking by Segmentation](https://mlsanity.com/paper/2203.11191/) (2022)

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

- [Generalized Relation Modeling for Transformer Tracking](https://mlsanity.com/paper/2303.16580/) (2023)
- [LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation](https://mlsanity.com/paper/2404.19326/) (2024)
- [Separable Self and Mixed Attention Transformers for Efficient Object Tracking](https://mlsanity.com/paper/2309.03979/) (2023)
- [Unifying Visual and Vision-Language Tracking via Contrastive Learning](https://mlsanity.com/paper/2401.11228/) (2024)
- [Beyond SOT: Tracking Multiple Generic Objects at Once](https://mlsanity.com/paper/2212.11920/) (2022)
- [Improving Visual Object Tracking through Visual Prompting](https://mlsanity.com/paper/2409.18901/) (2024)
- [DeTrack: In-model Latent Denoising Learning for Visual Object Tracking](https://mlsanity.com/paper/2501.02467/) (2025)
- [NT-VOT211: A Large-Scale Benchmark for Night-time Visual Object Tracking](https://mlsanity.com/paper/2410.20421/) (2024)
- [Improving Accuracy and Generalization for Efficient Visual Tracking](https://mlsanity.com/paper/2411.18855/) (2024)
- [Collaborating Vision, Depth, and Thermal Signals for Multi-Modal Tracking: Dataset and Algorithm](https://mlsanity.com/paper/2509.24741/) (2025)
- [AViTMP: A Tracking-Specific Transformer for Single-Branch Visual Tracking](https://mlsanity.com/paper/2310.19542/) (2023)
- [UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions](https://mlsanity.com/paper/2507.00648/) (2025)
- [UniSOT: A Unified Framework for Multi-Modality Single Object Tracking](https://mlsanity.com/paper/2511.01427/) (2025)
- [Progressive Scaling Visual Object Tracking](https://mlsanity.com/paper/2505.19990/) (2025)
- [Is Tracking really more challenging in First Person Egocentric Vision?](https://mlsanity.com/paper/2507.16015/) (2025)
- [GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing](https://mlsanity.com/paper/2602.08550/) (2026)
- [Robust Visual Tracking by Motion Analyzing](https://mlsanity.com/paper/2309.03247/) (2023)
- [Depth Attention for Robust RGB Tracking](https://mlsanity.com/paper/2410.20395/) (2024)
- [Exploring Reliable Spatiotemporal Dependencies for Efficient Visual Tracking](https://mlsanity.com/paper/2601.09078/) (2026)
- [GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2602.14771/) (2026)
- [Beyond MACs: Hardware Efficient Architecture Design for Vision Backbones](https://mlsanity.com/paper/2603.26551/) (2026)
- [SFDATrack: Generalized Source-Free Domain Adaptive Tracking Under Adverse Weather Conditions](https://mlsanity.com/paper/2607.00369/) (2026)

## Abstract (arXiv)

One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks. While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as before with recent trackers achieving higher performance mainly due to (i) the introduction of more sophisticated transformers-based methods and (ii) the lack of diverse scenarios with adverse visibility such as, severe weather conditions, camouflage and imaging effects. We introduce AVisT, a dedicated benchmark for visual tracking in diverse scenarios with adverse visibility. AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios broadly grouped into five attributes with 42 object categories. The key contribution of AVisT is diverse and challenging scenarios covering severe weather conditions such as, dense fog, heavy rain and sandstorm; obstruction effects including, fire, sun glare and splashing water; adverse imaging effects such as, low-light; target effects including, small targets and distractor objects along with camouflage. We further benchmark 17 popular and recent trackers on AVisT with detailed analysis of their tracking performance across attributes, demonstrating a big room for improvement in performance. We believe that AVisT can greatly benefit the tracking community by complementing the existing benchmarks, in developing new creative tracking solutions in order to continue pushing the boundaries of the state-of-the-art. Our dataset along with the complete tracking performance evaluation is available at: https://github.com/visionml/pytracking