Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

166
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
166
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

86
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
86

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

How Good Is the Machine at the Imitation Game? On Stylistic Characteristics of AI-Generated Images.

Journal of imaging·2025
Same author

A data-driven approach to interfacial polymerization exploiting machine learning for predicting thin-film composite membrane formation.

Materials horizons·2025
Same author

Starfish-inspired wearable bioelectronic systems for physiological signal monitoring during motion and real-time heart disease diagnosis.

Science advances·2025
Same author

On the Dynamism of Paintings Through the Distribution of Edge Directions.

Journal of imaging·2024
Same author

Hardware-accelerated integrated optoelectronic platform towards real-time high-resolution hyperspectral video understanding.

Nature communications·2024
Same author

Ego4D: Around the World in 3,600 Hours of Egocentric Video.

IEEE transactions on pattern analysis and machine intelligence·2024

Related Experiment Video

Updated: Sep 7, 2025

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

153

Scaling up SoccerNet with multi-view spatial localization and re-identification.

Anthony Cioppa1, Adrien Deliège2, Silvio Giancola3

  • 1University of Liège, Montefiore Institute, Quartier Polytech 1, Allée de la découverte 1, 4000, Liège, Belgium. anthony.cioppa@uliege.be.

Scientific Data
|June 21, 2022
PubMed
Summary

SoccerNet-v3 enhances soccer analysis with extensive spatial annotations and cross-view player correspondences. This largest multi-view dataset aids computer vision tasks like player re-identification and augmented reality applications.

More Related Videos

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

490

Related Experiment Videos

Last Updated: Sep 7, 2025

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

153
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

490

Area of Science:

  • Computer Vision
  • Sports Analytics

Background:

  • Soccer videos offer rich data for computer vision analysis.
  • Existing datasets lack comprehensive spatial and multi-view information.

Purpose of the Study:

  • Introduce SoccerNet-v3, an expanded dataset for detailed soccer video analysis.
  • Provide extensive spatial annotations and cross-view correspondences for enhanced computer vision tasks.

Main Methods:

  • Annotated live and replay soccer action frames with detailed local information.
  • Labeled lines, goal parts, players, referees, teams, objects, and jersey numbers.
  • Established player correspondences across different video viewpoints.

Main Results:

  • Created SoccerNet-v3, the largest dataset for multi-view soccer analysis.
  • Generated 1,324,732 annotations across 33,986 soccer images.
  • Included Python code for easy data access and download.

Conclusions:

  • SoccerNet-v3 supports advanced computer vision tasks in soccer.
  • Facilitates applications in augmented reality and sports analytics.
  • Enables fine-grained automated analysis of soccer matches.