AuthorsO. A. N. Rongved, S. Hicks, V. Thambawita, H. K. Stensland, E. Zouganeli, D. Johansen, M. Riegler and P. Halvorsen
TitleReal-Time Detection of Events in Soccer Videos using 3D Convolutional Neural Networks
AfilliationMachine Learning
Project(s)Department of Holistic Systems
Publication TypeProceedings, refereed
Year of Publication2020
Conference Name2020 IEEE International Symposium on Multimedia (ISM)

In this paper, we present an algorithm for automatically detecting events in soccer videos using 3D convolutional neural networks. The algorithm uses a sliding window approach to scan over a given video to detect events such as goals, yellow/red cards, and player substitutions. We test the method on three different datasets from SoccerNet, the Swedish  Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results. 

Citation Key27557