Soccer Event Detection Using Deep Learning

TL;DR

This paper presents a deep learning approach for soccer event detection, focusing on classifying images of red and yellow cards and other key events, using a new dataset and achieving superior performance over existing methods.

Contribution

Introduces a multi-module deep learning framework and a new dataset for soccer event detection, improving accuracy in classifying specific soccer events from video frames.

Findings

Achieves higher detection accuracy than state-of-the-art methods.

Effectively distinguishes between red and yellow card images.

Demonstrates robustness across UEFA Champions League match data.

Abstract

Event detection is an important step in extracting knowledge from the video. In this paper, we propose a deep learning approach to detect events in a soccer match emphasizing the distinction between images of red and yellow cards and the correct detection of the images of selected events from other images. This method includes the following three modules: i) the variational autoencoder (VAE) module to differentiate between soccer images and others image, ii) the image classification module to classify the images of events, and iii) the fine-grain image classification module to classify the images of red and yellow cards. Additionally, a new dataset was introduced for soccer images classification that is employed to train the networks mentioned in the paper. In the final section, 10 UEFA Champions League matches are used to evaluate the networks' performance and precision in detecting the events. The experiments demonstrate that the proposed method achieves better performance than state-of-the-art methods.