# PLayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips

arXiv: https://arxiv.org/abs/2407.16076 · HTML: https://mlsanity.com/paper/2407.16076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 52 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 21 of 21)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [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)
- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes](https://mlsanity.com/paper/2304.05170/) (2023)
- [SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos](https://mlsanity.com/paper/2204.06918/) (2022)
- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
- [Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)](https://mlsanity.com/paper/2001.00139/) (2020)
- [PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR System](https://mlsanity.com/paper/2109.03144/) (2021)
- [Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports](https://mlsanity.com/paper/2306.13074/) (2023)
- [GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation](https://mlsanity.com/paper/2303.14655/) (2023)
- [A Survey on Content-Aware Video Analysis for Sports](https://mlsanity.com/paper/1703.01170/) (2017)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [SoccerNet 2023 Challenges Results](https://mlsanity.com/paper/2309.06006/) (2023)
- [Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives](https://mlsanity.com/paper/2309.12067/) (2023)
- [MMSys'22 Grand Challenge on AI-based Video Production for Soccer](https://mlsanity.com/paper/2202.01031/) (2022)
- [Slicing and dicing soccer: automatic detection of complex events from spatio-temporal data](https://mlsanity.com/paper/2004.04147/) (2020)
- [Optical tracking in team sports](https://mlsanity.com/paper/2204.04143/) (2022)
- [Soccer on Social Media](https://mlsanity.com/paper/2310.12328/) (2023)

## Abstract (arXiv)

In the rapidly evolving field of sports analytics, the automation of targeted video processing is a pivotal advancement. We propose PlayerTV, an innovative framework which harnesses state-of-the-art AI technologies for automatic player tracking and identification in soccer videos. By integrating object detection and tracking, Optical Character Recognition (OCR), and color analysis, PlayerTV facilitates the generation of player-specific highlight clips from extensive game footage, significantly reducing the manual labor traditionally associated with such tasks. Preliminary results from the evaluation of our core pipeline, tested on a dataset from the Norwegian Eliteserien league, indicate that PlayerTV can accurately and efficiently identify teams and players, and our interactive Graphical User Interface (GUI) serves as a user-friendly application wrapping this functionality for streamlined use.