Towards Universal Soccer Video Understanding
Jiayuan Rao, Haoning Wu, Hao Jiang, Ya Zhang, Yanfeng Wang, Weidi Xie
2024-12-06
Summary
This paper talks about a new framework for understanding soccer videos called SoccerReplay-1988 and a model named MatchVision, which helps analyze and interpret events in soccer matches using advanced technology.
What's the problem?
Soccer is a popular sport worldwide, but analyzing soccer videos to understand the game better is challenging. Existing methods often lack the ability to accurately capture and interpret the complex actions and events that occur during matches, making it hard for fans, coaches, and analysts to gain insights from the footage.
What's the solution?
The authors introduced SoccerReplay-1988, the largest dataset of soccer videos with detailed annotations from 1,988 matches. They also developed MatchVision, the first visual-language model specifically designed for soccer. This model uses information from both the visuals and audio of the games to perform tasks like classifying events, generating commentary, and recognizing fouls. Through extensive testing, MatchVision showed better performance than previous models in these areas.
Why it matters?
This research is important because it improves how we can analyze soccer matches using technology. By providing a comprehensive dataset and a powerful model, it allows for better understanding of the game, which can help coaches improve strategies, enhance fan engagement through richer content, and automate commentary generation. This work sets a new standard for sports analysis and could lead to more advanced applications in sports technology.
Abstract
As a globally celebrated sport, soccer has attracted widespread interest from fans all over the world. This paper aims to develop a comprehensive multi-modal framework for soccer video understanding. Specifically, we make the following contributions in this paper: (i) we introduce SoccerReplay-1988, the largest multi-modal soccer dataset to date, featuring videos and detailed annotations from 1,988 complete matches, with an automated annotation pipeline; (ii) we present the first visual-language foundation model in the soccer domain, MatchVision, which leverages spatiotemporal information across soccer videos and excels in various downstream tasks; (iii) we conduct extensive experiments and ablation studies on event classification, commentary generation, and multi-view foul recognition. MatchVision demonstrates state-of-the-art performance on all of them, substantially outperforming existing models, which highlights the superiority of our proposed data and model. We believe that this work will offer a standard paradigm for sports understanding research.