Soccer Event Detection Using Deep Learning
This work addresses the problem of automated soccer event detection for sports analytics and broadcasting, offering an incremental improvement over existing methods.
This paper proposes a deep learning approach to detect soccer events, specifically distinguishing between red and yellow cards and other event images. The method was evaluated on 10 UEFA Champions League matches and reportedly achieved better performance than state-of-the-art methods.
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.