A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification

TL;DR

This paper introduces a graph-based approach for soccer action spotting that uses unsupervised player classification to improve the detection of key game events, achieving high accuracy and competitive performance.

Contribution

It presents a novel graph-based method that models players and their interactions for action spotting, with unsupervised player classification and improved accuracy over existing methods.

Findings

Player classification accuracy of 97.72%

Action spotting average-mAP of 57.83%

Outperforms similar graph-based methods

Abstract

Action spotting in soccer videos is the task of identifying the specific time when a certain key action of the game occurs. Lately, it has received a large amount of attention and powerful methods have been introduced. Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. Most approaches have focused on the latter, given that their models exploit the global visual features of the sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification, or player classification task, we obtain an accuracy of 97.72% in our annotated benchmark. For the action spotting task, our method obtains an overall performance of 57.83% average-mAP by combining it with other audiovisual modalities. This performance surpasses similar graph-based methods and has competitive results with heavy computing methods. Code and data are available at https://github.com/IPCV/soccer_action_spotting.