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Spiideo SoccerNet SynLoc : Single Frame World Coordinate Athlete Detection and Localization with Synthetic Data

Ardö, Håkan LU ; Nilsson, Mikael LU orcid ; Cioppa, Anthony ; Magera, Floriane ; Giancola, Silvio ; Liu, Haochen ; Ghanem, Bernard and Van Droogenbroeck, Marc (2025) 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2025 p.278-285
Abstract

Currently, most research and public datasets for video sports analytics are base on detecting players as bounding boxes in broadcast videos. Going from there to precise locations on the pitch is however hard. Modern solutions are making dedicated static cameras covering the entire pitch more readily accessible, and they are now used more and more even in lower tiers. To promote research that can take benefits of such cameras and produce more precise pitch locations, we introduce the Spiideo SoccerNet SynLoc dataset. It consists of synthetic athletes rendered on top of images from real world installation of such cameras. We also introduce a new task of detecting the players in the world pitch coordinate system and a new metric based... (More)

Currently, most research and public datasets for video sports analytics are base on detecting players as bounding boxes in broadcast videos. Going from there to precise locations on the pitch is however hard. Modern solutions are making dedicated static cameras covering the entire pitch more readily accessible, and they are now used more and more even in lower tiers. To promote research that can take benefits of such cameras and produce more precise pitch locations, we introduce the Spiideo SoccerNet SynLoc dataset. It consists of synthetic athletes rendered on top of images from real world installation of such cameras. We also introduce a new task of detecting the players in the world pitch coordinate system and a new metric based solely on real world physical properties where the representation in the image is irrelevant. The dataset and code are publicly available at https://github.com/Spiideo/sskit.

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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
3D, Dataset, Detection, Human, Localization, Sports, Synthetic
host publication
Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
pages
8 pages
conference name
20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2025
conference location
Porto, Portugal
conference dates
2025-02-26 - 2025-02-28
external identifiers
  • scopus:105001840108
DOI
10.5220/0013108200003912
language
English
LU publication?
yes
id
d763ec66-e94a-46dc-9126-cef74ce67862
date added to LUP
2025-09-01 13:28:21
date last changed
2025-09-01 13:29:04
@inproceedings{d763ec66-e94a-46dc-9126-cef74ce67862,
  abstract     = {{<p>Currently, most research and public datasets for video sports analytics are base on detecting players as bounding boxes in broadcast videos. Going from there to precise locations on the pitch is however hard. Modern solutions are making dedicated static cameras covering the entire pitch more readily accessible, and they are now used more and more even in lower tiers. To promote research that can take benefits of such cameras and produce more precise pitch locations, we introduce the Spiideo SoccerNet SynLoc dataset. It consists of synthetic athletes rendered on top of images from real world installation of such cameras. We also introduce a new task of detecting the players in the world pitch coordinate system and a new metric based solely on real world physical properties where the representation in the image is irrelevant. The dataset and code are publicly available at https://github.com/Spiideo/sskit.</p>}},
  author       = {{Ardö, Håkan and Nilsson, Mikael and Cioppa, Anthony and Magera, Floriane and Giancola, Silvio and Liu, Haochen and Ghanem, Bernard and Van Droogenbroeck, Marc}},
  booktitle    = {{Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}},
  keywords     = {{3D; Dataset; Detection; Human; Localization; Sports; Synthetic}},
  language     = {{eng}},
  pages        = {{278--285}},
  title        = {{Spiideo SoccerNet SynLoc : Single Frame World Coordinate Athlete Detection and Localization with Synthetic Data}},
  url          = {{http://dx.doi.org/10.5220/0013108200003912}},
  doi          = {{10.5220/0013108200003912}},
  year         = {{2025}},
}