Deep Understanding of Soccer Match Videos
Shikun Xu, Yandong Zhu, Gen Li, Changhu Wang

TL;DR
This paper presents a comprehensive computer vision system that analyzes soccer match videos to detect objects, track movements, recognize players, classify scenes, and generate highlights, thereby enriching viewer understanding and engagement.
Contribution
The novel system integrates multiple vision techniques to provide detailed, real-time analysis and summaries of soccer matches from live video streams.
Findings
Effective detection of key objects like players and ball
Accurate tracking of player and ball movements
Generation of highlights and tactical visualizations
Abstract
Soccer is one of the most popular sport worldwide, with live broadcasts frequently available for major matches. However, extracting detailed, frame-by-frame information on player actions from these videos remains a challenge. Utilizing state-of-the-art computer vision technologies, our system can detect key objects such as soccer balls, players and referees. It also tracks the movements of players and the ball, recognizes player numbers, classifies scenes, and identifies highlights such as goal kicks. By analyzing live TV streams of soccer matches, our system can generate highlight GIFs, tactical illustrations, and diverse summary graphs of ongoing games. Through these visual recognition techniques, we deliver a comprehensive understanding of soccer game videos, enriching the viewer's experience with detailed and insightful analysis.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Analysis and Summarization · Sports Analytics and Performance · Human Pose and Action Recognition
