Using 3D Convolutional Neural Networks for Real-time Detection of Soccer Events

Author:

Rongved Olav A. Nergård12,Hicks Steven A.12,Thambawita Vajira12,Stensland Håkon K.3,Zouganeli Evi2,Johansen Dag4,Midoglu Cise1,Riegler Michael A.1,Halvorsen Pål125

Affiliation:

1. SimulaMet, Oslo, Norway

2. Oslo Metropolitan University, Oslo, Norway

3. Simula Research Laboratory, Fornebu, Norway

4. UiT The Arctic University of Norway Tromsø, Norway

5. Forzasys AS, Oslo, Norway

Abstract

Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm 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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Linguistics and Language,Information Systems,Software

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