Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) |
| Seiten | 1463-1473 |
| Seitenumfang | 11 |
| ISBN (elektronisch) | 978-1-6654-0915-5 |
| Publikationsstatus | Veröffentlicht - 2022 |
Publikationsreihe
| Name | IEEE Winter Conference on Applications of Computer Vision |
|---|---|
| ISSN (Print) | 2472-6737 |
| ISSN (elektronisch) | 2642-9381 |
Abstract
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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Angewandte Informatik
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- BibTex
- RIS
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2022. S. 1463-1473 ( IEEE Winter Conference on Applications of Computer Vision).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Extraction of Positional Player Data from Broadcast Soccer Videos
AU - Theiner, Jonas
AU - Gritz, Wolfgang
AU - Müller-Budack, Eric
AU - Rein, Robert
AU - Memmert, Daniel
AU - Ewerth, Ralph
N1 - Funding Information: This project has received funding from the German Federal Ministry of Education and Research (BMBF – Bundesministerium für Bildung und Forschung) under 01IS20021B and 01IS20021A.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Vision Systems and Applications
UR - http://www.scopus.com/inward/record.url?scp=85126108470&partnerID=8YFLogxK
U2 - 10.1109/WACV51458.2022.00153
DO - 10.1109/WACV51458.2022.00153
M3 - Conference contribution
SN - 978-1-6654-0916-2
T3 - IEEE Winter Conference on Applications of Computer Vision
SP - 1463
EP - 1473
BT - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
ER -