Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage

Autor:	Wear, Matthew, Beal, Ryan, Matthews, Tim, Norman, Tim, Ramchurn, Sarvapali
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	As an amateur goalkeeper playing grassroots soccer, who better to learn from than top professional goalkeepers? In this paper, we harness computer vision and machine learning models to appraise the save technique of professionals in a way those at lower levels can learn from. We train an unsupervised machine learning model using 3D body pose data extracted from broadcast footage to learn professional goalkeeper technique. Then, an "expected saves" model is developed, from which we can identify the optimal goalkeeper technique in different match contexts. Comment: 17 pages, 15 figures, MIT Sloan Sports Analytics Conference, March 4-5 2022, Boston, USA
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.12259 Zobrazit plný text záznamu View this record from Arxiv