# SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes

arXiv: https://arxiv.org/abs/2304.05170 · HTML: https://mlsanity.com/paper/2304.05170/

## Key facts

- Cited by 131 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 50 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild](https://mlsanity.com/paper/1803.10794/) (2018)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [Quasi-Dense Similarity Learning for Multiple Object Tracking](https://mlsanity.com/paper/2006.06664/) (2020)
- [DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion](https://mlsanity.com/paper/2111.14690/) (2021)
- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)

## Built on by (most cited first, 30 of 131)

- [MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking](https://mlsanity.com/paper/2307.15700/) (2023)
- [Multiple Object Tracking as ID Prediction](https://mlsanity.com/paper/2403.16848/) (2024)
- [DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction](https://mlsanity.com/paper/2403.02075/) (2024)
- [Merlin:Empowering Multimodal LLMs with Foresight Minds](https://mlsanity.com/paper/2312.00589/) (2023)
- [Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports](https://mlsanity.com/paper/2306.13074/) (2023)
- [FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment](https://mlsanity.com/paper/2405.06887/) (2024)
- [Towards Generalizable Multi-Object Tracking](https://mlsanity.com/paper/2406.00429/) (2024)
- [Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking](https://mlsanity.com/paper/2410.01806/) (2024)
- [MotionTrack: Learning Motion Predictor for Multiple Object Tracking](https://mlsanity.com/paper/2306.02585/) (2023)
- [MambaTrack: A Simple Baseline for Multiple Object Tracking with State Space Model](https://mlsanity.com/paper/2408.09178/) (2024)
- [BoostTrack++: using tracklet information to detect more objects in multiple object tracking](https://mlsanity.com/paper/2408.13003/) (2024)
- [Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs](https://mlsanity.com/paper/2501.04670/) (2025)
- [SportsHHI: A Dataset for Human-Human Interaction Detection in Sports Videos](https://mlsanity.com/paper/2404.04565/) (2024)
- [Multi-Object Tracking in the Dark](https://mlsanity.com/paper/2405.06600/) (2024)
- [Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach](https://mlsanity.com/paper/2408.00969/) (2024)
- [Omnidirectional Multi-Object Tracking](https://mlsanity.com/paper/2503.04565/) (2025)
- [Beyond MOT: Semantic Multi-Object Tracking](https://mlsanity.com/paper/2403.05021/) (2024)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos](https://mlsanity.com/paper/2404.13868/) (2024)
- [SoccerMaster: A Vision Foundation Model for Soccer Understanding](https://mlsanity.com/paper/2512.11016/) (2025)
- [ETTrack: Enhanced Temporal Motion Predictor for Multi-Object Tracking](https://mlsanity.com/paper/2405.15755/) (2024)
- [LaMOT: Language-Guided Multi-Object Tracking](https://mlsanity.com/paper/2406.08324/) (2024)
- [Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking](https://mlsanity.com/paper/2402.09865/) (2024)
- [MambaMOT: State-Space Model as Motion Predictor for Multi-Object Tracking](https://mlsanity.com/paper/2403.10826/) (2024)
- [Z-GMOT: Zero-shot Generic Multiple Object Tracking](https://mlsanity.com/paper/2305.17648/) (2023)
- [Foul prediction with estimated poses from soccer broadcast video](https://mlsanity.com/paper/2402.09650/) (2024)
- [OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccer](https://mlsanity.com/paper/2502.02785/) (2025)
- [Hierarchical and Multimodal Data for Daily Activity Understanding](https://mlsanity.com/paper/2504.17696/) (2025)
- [Player-Centric Multimodal Prompt Generation for Large Language Model Based Identity-Aware Basketball Video Captioning](https://mlsanity.com/paper/2507.20163/) (2025)
- [TrackSSM: A General Motion Predictor by State-Space Model](https://mlsanity.com/paper/2409.00487/) (2024)

## Abstract (arXiv)

Multi-object tracking in sports scenes plays a critical role in gathering players statistics, supporting further analysis, such as automatic tactical analysis. Yet existing MOT benchmarks cast little attention on the domain, limiting its development. In this work, we present a new large-scale multi-object tracking dataset in diverse sports scenes, coined as SportsMOT, where all players on the court are supposed to be tracked. It consists of 240 video sequences, over 150K frames (almost 15× MOT17) and over 1.6M bounding boxes (3× MOT17) collected from 3 sports categories, including basketball, volleyball and football. Our dataset is characterized with two key properties: 1) fast and variable-speed motion and 2) similar yet distinguishable appearance. We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association. We benchmark several state-of-the-art trackers and reveal the key challenge of SportsMOT lies in object association. To alleviate the issue, we further propose a new multi-object tracking framework, termed as MixSort, introducing a MixFormer-like structure as an auxiliary association model to prevailing tracking-by-detection trackers. By integrating the customized appearance-based association with the original motion-based association, MixSort achieves state-of-the-art performance on SportsMOT and MOT17. Based on MixSort, we give an in-depth analysis and provide some profound insights into SportsMOT. The dataset and code will be available at https://deeperaction.github.io/datasets/sportsmot.html.