SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap

Somers, Vladimir; Joos, Victor; Cioppa, Anthony; Giancola, Silvio; Ghasemzadeh, Seyed Abolfazl; Magera, Floriane; Standaert, Baptiste; Mansourian, Amir M.; Zhou, Xin; Kasaei, Shohreh; Ghanem, Bernard; Alahi, Alexandre; Van Droogenbroeck, Marc; De Vleeschouwer, Christophe

doi:10.1109/cvprw63382.2024.00334

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2024

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Cited by 4 publications

References 85 publications

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DCTracker: Rethinking MOT in soccer events under dual views via cascade association

Hu,

Zhang,

et al. 2024

Knowledge-Based Systems

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DCTracker: Rethinking MOT in soccer events under dual views via cascade association

Hu,

Zhang,

et al. 2024

Knowledge-Based Systems

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Unsupervised Clustering in Football Analysis: A Color-Segmentation and Lighting Adaptation Approach

Pan,

Zhou,

Wang

et al. 2024

IEEE Access

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SoccerNet-Depth: a Scalable Dataset for Monocular Depth Estimation in Sports Videos

Leduc,

Cioppa,

Giancola

et al. 2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Monocular Depth Estimation (MDE) is fundamental in sports video understanding, enhancing augmented graphics, scene understanding, and game state reconstruction. Despite remarkable progress in autonomous driving and indoor scene understanding, there is currently a lack of MDE datasets tailored for sports. Furthermore, most existing datasets only focus on single images, disregarding the temporal aspect. In this work, we introduce the first video dataset for MDE in sports, SoccerNet-Depth, focusing on football and basketball videos. In particular, we leverage the graphic engine from video games to automatically extract video sequences and their associated depth maps, making our dataset easily scalable. Furthermore, we benchmark and fine-tune several state-of-the-art MDE methods on our dataset. Our analysis shows that MDE in sports is far from being solved, making our dataset a perfect playground for future research. Dataset and codes: https://github.com/SoccerNet/sn-depth.

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SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and Identification on a Minimap

Cited by 4 publications

References 85 publications

DCTracker: Rethinking MOT in soccer events under dual views via cascade association

DCTracker: Rethinking MOT in soccer events under dual views via cascade association

Unsupervised Clustering in Football Analysis: A Color-Segmentation and Lighting Adaptation Approach

SoccerNet-Depth: a Scalable Dataset for Monocular Depth Estimation in Sports Videos

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