Intelligent Soccer Event Detection and Highlights Generation with Broadcast Cues Integration

Anirudh Narayanan, Sergei Chuprov, Leon Reznik, Raman Zatsarenko, Dmitrii Korobeinikov

Abstract: In this paper, we present an innovative approach to automate key event detection and highlights generation from soccer match videostreams that allows to improve accuracy and reliability, as well as to reduce data consumption and training time. Our method segments the videostream into distinct frames based on camera angles and activities, and integrates intelligent video analytics with additional visual information provided by broadcasters. As our major novelty in comparison to other intelligent soccer video analysis approaches, we deploy a Multi-Class Image Classifier to segment the video into wide-angle overviews, close-ups, and in-game replays, which allows us to improve the event detection performance and the quality of generated highlights. As another major novelty, we leverage YOLOv8 to detect events such as bookings, substitutions, and goals based on the additional information portrayed by broadcasters during the game. We evaluate our approach and demonstrate its advantage, when the additional information from broadcasters is available, against existing ones that analyze only the actions happening in the scene itself, such as the players' current positions and their actions between the frames. We evaluate our pipeline on a real soccer game recording and compare the highlights it generates with the official highlights provided by the broadcaster. Our pipeline demonstrates ample performance and is able to detect all key events in the game.