Summary of Soccernet Game State Reconstruction: End-to-end Athlete Tracking and Identification on a Minimap, by Vladimir Somers et al.
SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap
by Vladimir Somers, Victor Joos, Anthony Cioppa, Silvio Giancola, Seyed Abolfazl Ghasemzadeh, Floriane Magera, Baptiste Standaert, Amir Mohammad Mansourian, Xin Zhou, Shohreh Kasaei, Bernard Ghanem, Alexandre Alahi, Marc Van Droogenbroeck, Christophe De Vleeschouwer
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel task called Game State Reconstruction, which aims to track and identify athletes on the pitch from video recordings captured by a single camera. The authors introduce SoccerNet-GSR, a dataset comprising 200 video sequences of 30 seconds each, annotated with millions of line points for pitch localization and camera calibration, as well as athlete positions on the pitch. They also propose GS-HOTA, a novel metric to evaluate game state reconstruction methods. Additionally, the paper presents an end-to-end baseline for game state reconstruction and demonstrates that the task is challenging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using videos from football games to figure out where players are on the field. This helps coaches and analysts understand team strategies and player performance. The authors created a big dataset with lots of information about the players, the camera, and the field. They also came up with new ways to measure how well this process works. This research can help us better understand sports and improve our understanding of player movements. |
