# Extraction of Positional Player Data from Broadcast Soccer Videos

arXiv: https://arxiv.org/abs/2110.11107 · HTML: https://mlsanity.com/paper/2110.11107/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 57 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 16 of 16)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting](https://mlsanity.com/paper/2104.09333/) (2021)
- [Sports Camera Calibration via Synthetic Data](https://mlsanity.com/paper/1810.10658/) (2018)
- [Optimizing Through Learned Errors for Accurate Sports Field Registration](https://mlsanity.com/paper/1909.08034/) (2019)
- [Real-Time Camera Pose Estimation for Sports Fields](https://mlsanity.com/paper/2003.14109/) (2020)
- [Contrastive Learning for Sports Video: Unsupervised Player Classification](https://mlsanity.com/paper/2104.10068/) (2021)
- [Fast Video Shot Transition Localization with Deep Structured Models](https://mlsanity.com/paper/1808.04234/) (2018)
- [Automated Top View Registration of Broadcast Football Videos](https://mlsanity.com/paper/1703.01437/) (2017)
- [Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks](https://mlsanity.com/paper/1705.08214/) (2017)
- [FootAndBall: Integrated player and ball detector](https://mlsanity.com/paper/1912.05445/) (2019)
- [Light Cascaded Convolutional Neural Networks for Accurate Player Detection](https://mlsanity.com/paper/1709.10230/) (2017)
- ["Does 4-4-2 exist?" -- An Analytics Approach to Understand and Classify Football Team Formations in Single Match Situations](https://mlsanity.com/paper/1910.00412/) (2019)

## Built on by (most cited first, 5 of 5)

- [SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap](https://mlsanity.com/paper/2404.11335/) (2024)
- [TVCalib: Camera Calibration for Sports Field Registration in Soccer](https://mlsanity.com/paper/2207.11709/) (2022)
- [Tracking Skiers from the Top to the Bottom](https://mlsanity.com/paper/2312.09723/) (2023)
- [Evaluating deep tracking models for player tracking in broadcast ice hockey video](https://mlsanity.com/paper/2205.10949/) (2022)
- [Towards Athlete Fatigue Assessment from Association Football Videos](https://mlsanity.com/paper/2604.05636/) (2026)

## Abstract (arXiv)

Computer-aided support and analysis are becoming increasingly important in the modern world of sports. The scouting of potential prospective players, performance as well as match analysis, and the monitoring of training programs rely more and more on data-driven technologies to ensure success. Therefore, many approaches require large amounts of data, which are, however, not easy to obtain in general. In this paper, we propose a pipeline for the fully-automated extraction of positional data from broadcast video recordings of soccer matches. In contrast to previous work, the system integrates all necessary sub-tasks like sports field registration, player detection, or team assignment that are crucial for player position estimation. The quality of the modules and the entire system is interdependent. A comprehensive experimental evaluation is presented for the individual modules as well as the entire pipeline to identify the influence of errors to subsequent modules and the overall result. In this context, we propose novel evaluation metrics to compare the output with ground-truth positional data.