# Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking

arXiv: https://arxiv.org/abs/2401.09942 · HTML: https://mlsanity.com/paper/2401.09942/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 56 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 26 of 26)

- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [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)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification](https://mlsanity.com/paper/2302.11813/) (2023)
- [Human Semantic Parsing for Person Re-identification](https://mlsanity.com/paper/1804.00216/) (2018)
- [Part-Aligned Bilinear Representations for Person Re-identification](https://mlsanity.com/paper/1804.07094/) (2018)
- [SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos](https://mlsanity.com/paper/2204.06918/) (2022)
- [Pose-guided Visible Part Matching for Occluded Person ReID](https://mlsanity.com/paper/2004.00230/) (2020)
- [Hard to Track Objects with Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Space](https://mlsanity.com/paper/2211.14317/) (2022)
- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
- [OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association](https://mlsanity.com/paper/2103.02440/) (2021)
- [VARS: Video Assistant Referee System for Automated Soccer Decision Making from Multiple Views](https://mlsanity.com/paper/2304.04617/) (2023)
- [DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations](https://mlsanity.com/paper/2208.08190/) (2022)
- [Semi-Supervised Training to Improve Player and Ball Detection in Soccer](https://mlsanity.com/paper/2204.06859/) (2022)
- [Player Tracking and Identification in Ice Hockey](https://mlsanity.com/paper/2110.03090/) (2021)
- [Contrastive Learning for Sports Video: Unsupervised Player Classification](https://mlsanity.com/paper/2104.10068/) (2021)
- [Efficient tracking of team sport players with few game-specific annotations](https://mlsanity.com/paper/2204.04049/) (2022)
- [Multi-task learning for jersey number recognition in Ice Hockey](https://mlsanity.com/paper/2108.07848/) (2021)
- [Self-Supervised Small Soccer Player Detection and Tracking](https://mlsanity.com/paper/2011.10336/) (2020)
- [DeepSportLab: a Unified Framework for Ball Detection, Player Instance Segmentation and Pose Estimation in Team Sports Scenes](https://mlsanity.com/paper/2112.00627/) (2021)
- [Discriminative and Efficient Label Propagation on Complementary Graphs for Multi-Object Tracking](https://mlsanity.com/paper/1504.01124/) (2015)

## Built on by (most cited first, 13 of 13)

- [X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model](https://mlsanity.com/paper/2404.06332/) (2024)
- [Keypoint Promptable Re-Identification](https://mlsanity.com/paper/2407.18112/) (2024)
- [SoccerMaster: A Vision Foundation Model for Soccer Understanding](https://mlsanity.com/paper/2512.11016/) (2025)
- [TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos](https://mlsanity.com/paper/2503.18282/) (2025)
- [Towards long-term player tracking with graph hierarchies and domain-specific features](https://mlsanity.com/paper/2502.21242/) (2025)
- [From Broadcast to Minimap: Achieving State-of-the-Art SoccerNet Game State Reconstruction](https://mlsanity.com/paper/2504.06357/) (2025)
- [Towards Athlete Fatigue Assessment from Association Football Videos](https://mlsanity.com/paper/2604.05636/) (2026)
- [SoccerNet 2026 Challenges Results](https://mlsanity.com/paper/2607.07320/) (2026)
- [Improving Weakly-supervised Video Instance Segmentation by Leveraging Spatio-temporal Consistency](https://mlsanity.com/paper/2408.16661/) (2024)
- [SoccerTrack v2: A Full-Pitch Panoramic Video Dataset for Game State Reconstruction and Ball Action Spotting](https://mlsanity.com/paper/2508.01802/) (2025)
- [TacSIm: A Dataset and Benchmark for Football Tactical Style Imitation](https://mlsanity.com/paper/2603.25199/) (2026)
- [TreeSoc: Tree-Structured Dynamic Reasoning and Tool Synergy for Soccer Video Understanding](https://mlsanity.com/paper/2607.10990/) (2026)
- [Beyond a Single Crop: Contextualized Embeddings for Player Re-Identification](https://mlsanity.com/paper/2610.13259/) (2026)

## Abstract (arXiv)

Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the non-linear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. Therefore, the ability to extract meaningful embeddings to represent players is crucial in developing an effective tracking and re-identification system. In this paper, a multi-purpose part-based person representation method, called PRTreID, is proposed that performs three tasks of role classification, team affiliation, and re-identification, simultaneously. In contrast to available literature, a single network is trained with multi-task supervision to solve all three tasks, jointly. The proposed joint method is computationally efficient due to the shared backbone. Also, the multi-task learning leads to richer and more discriminative representations, as demonstrated by both quantitative and qualitative results. To demonstrate the effectiveness of PRTreID, it is integrated with a state-of-the-art tracking method, using a part-based post-processing module to handle long-term tracking. The proposed tracking method outperforms all existing tracking methods on the challenging SoccerNet tracking dataset.