AI-Based Video Clipping of Soccer Events

Joakim Olav Valand,Cise Midoglu,Steven Alexander Hicks,Haris Kadragic,Michael Alexander Riegler,Pål Halvorsen,Dag Johansen,Vajira Lasantha Thambawita,Tomas Kupka

doi:10.3390/make3040049

Abstract

The current gold standard for extracting highlight clips from soccer games is the use of manual annotations and clippings, where human operators define the start and end of an event and trim away the unwanted scenes. This is a tedious, time-consuming, and expensive task, to the extent of being rendered infeasible for use in lower league games. In this paper, we aim to automate the process of highlight generation using logo transition detection, scene boundary detection, and optional scene removal. We experiment with various approaches, using different neural network architectures on different datasets, and present two models that automatically find the appropriate time interval for extracting goal events. These models are evaluated both quantitatively and qualitatively, and the results show that we can detect logo and scene transitions with high accuracy and generate highlight clips that are highly acceptable for viewers. We conclude that there is considerable potential in automating the overall soccer video clipping process.

Highlights

Sports broadcasting and streaming are becoming increasingly popular, and the interest for viewing videos from sports events grows daily
After experimenting with a variety of Machine Learning (ML) models, we present a system that can find logo transitions using models based on ResNet [8] and VGG [9] and undertake scene boundary detection using a TransNet V2 [10] model pre-trained on the ClipShots [11] and IACC.3 [12] datasets
An artificial intelligence (AI)-based production pipeline can be of great value for broadcasters and streaming services that provide sports videos

Summary

Introduction

Sports broadcasting and streaming are becoming increasingly popular, and the interest for viewing videos from sports events grows daily. The amount of content worldwide, such as footage, event highlights, goal and player statistics, scores, and rankings, is enormous, not to mention rapidly growing, and there is a huge interest from numerous actors to consume this available content. In this respect, it is important to provide game summaries, as has been done for decades, and more recently, to dedicate streams for particular categories of events, such as goals, cards, saves, and penalties. The process of generating summaries, event highlights, and tags is often performed redundantly by different actors for different purposes

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