short-paper

Automatic player detection, tracking and mapping to field model for broadcast soccer videos

Authors:

Quang Tran,

Bac Vo,

Tien Dinh,

Duc DuongAuthors Info & Claims

MoMM '11: Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia

Pages 240 - 243

https://doi.org/10.1145/2095697.2095745

Published: 05 December 2011 Publication History

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Abstract

We present an automatic soccer analysis framework through detecting, tracking players, and reflecting the information into the field model. First, a player detector is built on four-seed edge feature approach which extracts player regions applied in long view shots including playfield extraction, shot view classification, and player segmentation. Second, a multi-player tracker uses a high general reversible jump Markov chain Monte Carlo (RJMCMC)-based approach to associate player regions detected in each frame. Third, a fast calibration algorithm matches field model by fitting two regions: the center circle and the penalty areas so as to provide the geometry transformation enabled to map player positions in a video frame to the real-world coordinate. The experimental results on broadcast videos of the 2010 FIFA World Cup South Africa have evaluated to prove the effective design and the promise of the proposed framework.

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Cited By

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Cioppa AGiancola SDeliege AKang LZhou XCheng ZGhanem BVan Droogenbroeck M(2022)SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00393(3490-3501)Online publication date: Jun-2022
https://doi.org/10.1109/CVPRW56347.2022.00393

Index Terms

Automatic player detection, tracking and mapping to field model for broadcast soccer videos
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding

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Published In

MoMM '11: Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia

December 2011

318 pages

ISBN:9781450307857

DOI:10.1145/2095697

Conference Chair:
Wenny Rahayu
La Trobe University, Australia
,
General Chairs:
Eric Pardede
La Trobe University, Australia
,
Nguyen Duc Cuong
International University (VNU-HCMC), Vietnam
,
Program Chair:
David Taniar
Monash University, Australia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 December 2011

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MoMM '11

MoMM '11: The 9th International Conference on Advances in Mobile Computing & Multimedia

December 5 - 7, 2011

Ho Chi Minh City, Vietnam

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Cioppa AGiancola SDeliege AKang LZhou XCheng ZGhanem BVan Droogenbroeck M(2022)SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00393(3490-3501)Online publication date: Jun-2022
https://doi.org/10.1109/CVPRW56347.2022.00393

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Self-Supervised Small Soccer Player Detection and Tracking

Automatic player labeling, tracking and field registration and trajectory mapping in broadcast soccer video

Automatic player detection, labeling and tracking in broadcast soccer video