Abstract

Soccer fans often prefer watching summaries of football games due to the significant time commitment required to view an entire match. Traditional manual methods for analyzing and extracting exciting clips are tedious and time consuming. Therefore, automate the process of video analysis and summarization is crucial. This paper presents a novel approach for automated soccer video summarization by classifying soccer events: card, corner, foul, and freekick. We implemented an empirical analysis of a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) architecture. The proposed CNN-GRU model achieved an outstanding accuracy of 99.3% and a validation accuracy of 95.18%. These results demonstrate the effectiveness of our approach in automated the extraction of important soccer events, offering significant improvements in efficiency and accuracy over traditional methods. This work has broad applications in sports video analysis and accurate generation of game highlights.