# SUTrack: Towards Simple and Unified Single Object Tracking

arXiv: https://arxiv.org/abs/2412.19138 · HTML: https://mlsanity.com/paper/2412.19138/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 126 references that are in the map.
- First posted on arXiv 26 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric](https://mlsanity.com/paper/2211.11010/) (2022)
- [VastTrack: Vast Category Visual Object Tracking](https://mlsanity.com/paper/2403.03493/) (2024)
- [Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking](https://mlsanity.com/paper/2304.14394/) (2023)
- [Describe and Attend to Track: Learning Natural Language guided Structural Representation and Visual Attention for Object Tracking](https://mlsanity.com/paper/1811.10014/) (2018)
- [Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers](https://mlsanity.com/paper/1912.02048/) (2019)

## Built on by (most cited first, 30 of 52)

- [MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2508.05630/) (2025)
- [What You Have is What You Track: Adaptive and Robust Multimodal Tracking](https://mlsanity.com/paper/2507.05899/) (2025)
- [CSTrack: Enhancing RGB-X Tracking via Compact Spatiotemporal Features](https://mlsanity.com/paper/2505.19434/) (2025)
- [ATCTrack: Aligning Target-Context Cues with Dynamic Target States for Robust Vision-Language Tracking](https://mlsanity.com/paper/2507.19875/) (2025)
- [ReasoningTrack: Chain-of-Thought Reasoning for Long-term Vision-Language Tracking](https://mlsanity.com/paper/2508.05221/) (2025)
- [Video Understanding: From Geometry and Semantics to Unified Models](https://mlsanity.com/paper/2603.17840/) (2026)
- [Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking](https://mlsanity.com/paper/2506.20381/) (2025)
- [VPTracker: Global Vision-Language Tracking via Visual Prompt](https://mlsanity.com/paper/2512.22799/) (2025)
- [RELO: Reinforcement Learning to Localize for Visual Object Tracking](https://mlsanity.com/paper/2605.07379/) (2026)
- [Omni Survey for Multimodality Analysis in Visual Object Tracking](https://mlsanity.com/paper/2508.13000/) (2025)
- [Hyperspectral Mamba for Hyperspectral Object Tracking](https://mlsanity.com/paper/2509.08265/) (2025)
- [Group Orthogonal Low-Rank Adaptation for RGB-T Tracking](https://mlsanity.com/paper/2512.05359/) (2025)
- [UETrack: A Unified and Efficient Framework for Single Object Tracking](https://mlsanity.com/paper/2603.01412/) (2026)
- [RAGTrack: Language-aware RGBT Tracking with Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.03617/) (2026)
- [UASTrack: A Unified Adaptive Selection Framework with Modality-Customization in Single Object Tracking](https://mlsanity.com/paper/2502.18220/) (2025)
- [Dynamic Graph Induced Contour-aware Heat Conduction Network for Event-based Object Detection](https://mlsanity.com/paper/2505.12908/) (2025)
- [Learning Frequency and Memory-Aware Prompts for Multi-Modal Object Tracking](https://mlsanity.com/paper/2506.23972/) (2025)
- [SAM 2++: Tracking Anything at Any Granularity](https://mlsanity.com/paper/2510.18822/) (2025)
- [SwiTrack: Tri-State Switch for Cross-Modal Object Tracking](https://mlsanity.com/paper/2511.16227/) (2025)
- [Tracking and Segmenting Anything in Any Modality](https://mlsanity.com/paper/2511.19475/) (2025)
- [UAUTrack: Towards Unified Multimodal Anti-UAV Visual Tracking](https://mlsanity.com/paper/2512.02668/) (2025)
- [Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding](https://mlsanity.com/paper/2512.06673/) (2025)
- [How Far are Modern Trackers from UAV-Anti-UAV? A Million-Scale Benchmark and New Baseline](https://mlsanity.com/paper/2512.07385/) (2025)
- [Decoupling Amplitude and Phase Attention in Frequency Domain for RGB-Event based Visual Object Tracking](https://mlsanity.com/paper/2601.01022/) (2026)
- [GLAD: Generative Language-Assisted Visual Tracking for Low-Semantic Templates](https://mlsanity.com/paper/2602.00570/) (2026)
- [GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing](https://mlsanity.com/paper/2602.08550/) (2026)
- [Uni-MDTrack: Learning Decoupled Memory and Dynamic States for Parameter-Efficient Visual Tracking in All Modality](https://mlsanity.com/paper/2603.14452/) (2026)
- [Unified Multimodal Visual Tracking with Dual Mixture-of-Experts](https://mlsanity.com/paper/2605.03716/) (2026)
- [VL-UniTrack: A Unified Framework with Visual-Language Prompts for UAV-Ground Visual Tracking](https://mlsanity.com/paper/2605.04574/) (2026)
- [Spatio-Temporal Conditional Denoising Transformer for Modality-Missing RGBT Tracking](https://mlsanity.com/paper/2607.24701/) (2026)

## Abstract (arXiv)

In this paper, we propose a simple yet unified single object tracking (SOT) framework, dubbed SUTrack. It consolidates five SOT tasks (RGB-based, RGB-Depth, RGB-Thermal, RGB-Event, RGB-Language Tracking) into a unified model trained in a single session. Due to the distinct nature of the data, current methods typically design individual architectures and train separate models for each task. This fragmentation results in redundant training processes, repetitive technological innovations, and limited cross-modal knowledge sharing. In contrast, SUTrack demonstrates that a single model with a unified input representation can effectively handle various common SOT tasks, eliminating the need for task-specific designs and separate training sessions. Additionally, we introduce a task-recognition auxiliary training strategy and a soft token type embedding to further enhance SUTrack's performance with minimal overhead. Experiments show that SUTrack outperforms previous task-specific counterparts across 11 datasets spanning five SOT tasks. Moreover, we provide a range of models catering edge devices as well as high-performance GPUs, striking a good trade-off between speed and accuracy. We hope SUTrack could serve as a strong foundation for further compelling research into unified tracking models. Code and models are available at github.com/chenxin-dlut/SUTrack.