Title
Individual action and group activity recognition in soccer videos. Master's thesis
Author
Gerats, B.G.A.
Publication year
2020
Abstract
Data and statistics are key to soccer analytics and have important roles in player evaluation and fan engagement. Automatic recognition of soccer events - such as passes and corners - would ease the data gathering process, potentially opening up the market for non-professional soccer analytics. We propose a novel method for the automatic recognition of soccer events from video. To the best of our knowledge, it is the first method that infers both individual actions and group activities simultaneously from soccer videos. Three key contributions in the proposed method are (1) the use of player-centric snippets as model input, (2) per-player feature extraction with an I3D CNN - based on RGB video and optical flow - and (3) the use of feature suppression and zero-padding in graph attention networks for feature contextualisation. The results show that the proposed method performs better than an alternative state-of-the-art method, designed for action and activity recognition in volleyball. Our method gains 98.7% accuracy for the recognition of eight actions and 75.2% for eleven activities.
Subject
Action recognition
Group activity recognition
Soccer match events
Player snippets
To reference this document use:
http://resolver.tudelft.nl/uuid:b48fa943-4eb0-48b7-8fd4-28b31571ab17
TNO identifier
946763
Publisher
University of Twente
Bibliographical note
begeleiding vanuit TNO: H.Dol W.R. Uijens
Document type
book