Next Article in Journal
On the Use of Population Data for Training Seasonal Local Models-Based Glucose Predictors: An In Silico Study
Previous Article in Journal
A REM Update Methodology Based on Clustering and Random Forest
Previous Article in Special Issue
Machines Perceive Emotions: Identifying Affective States from Human Gait Using On-Body Smart Devices
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 13
Issue 9
10.3390/app13095361
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios

by
Xubo Fu
1,†,
Wenbin Huang
2,3,4,†,
Yaoran Sun
4,
Xinhua Zhu
5,
Julian Evans
2,3,
Xian Song
6,
Tongyu Geng
2,3 and
Sailing He
2,3,4,5,*
1
Department of Public Physical and Art Education, Zhejiang University, Hangzhou 310058, China
2
National Engineering Research Center for Optical Instruments, Zhejiang University, Hangzhou 310058, China
3
Centre for Optical and Electromagnetic Research, Zhejiang University, Hangzhou 310058, China
4
Hangzhou Zhuxing Information Technology Co., Ltd., Hangzhou 311100, China
5
Shanghai Institute for Advanced Study, Zhejiang University, Shanghai 200135, China
6
Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong SAR, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2023, 13(9), 5361; https://doi.org/10.3390/app13095361
Submission received: 12 February 2023 / Revised: 17 April 2023 / Accepted: 20 April 2023 / Published: 25 April 2023
(This article belongs to the Special Issue Wearable Sensing and Computing Technologies for Health and Sports)
Browse Figures
Versions Notes

Abstract

Localization and tracking in multi-player sports present significant challenges, particularly in wide and crowded scenes where severe occlusions can occur. Traditional solutions relying on a single camera are limited in their ability to accurately identify players and may result in ambiguous detection. To overcome these challenges, we proposed fusing information from multiple cameras positioned around the field to improve positioning accuracy and eliminate occlusion effects. Specifically, we focused on soccer, a popular and representative multi-player sport, and developed a multi-view recording system based on a 1+N strategy. This system enabled us to construct a new benchmark dataset and continuously collect data from several sports fields. The dataset includes 17 sets of densely annotated multi-view videos, each lasting 2 min, as well as 1100+ min multi-view videos. It encompasses a wide range of game types and nearly all scenarios that could arise during real game tracking. Finally, we conducted a thorough assessment of four multi-view multi-object tracking (MVMOT) methods and gained valuable insights into the tracking process in actual games.
Keywords: multi-view; tracking; soccer; system; benchmark multi-view; tracking; soccer; system; benchmark

Share and Cite

MDPI and ACS Style

Fu, X.; Huang, W.; Sun, Y.; Zhu, X.; Evans, J.; Song, X.; Geng, T.; He, S. A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios. Appl. Sci. 2023, 13, 5361. https://doi.org/10.3390/app13095361

AMA Style

Fu X, Huang W, Sun Y, Zhu X, Evans J, Song X, Geng T, He S. A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios. Applied Sciences. 2023; 13(9):5361. https://doi.org/10.3390/app13095361

Chicago/Turabian Style

Fu, Xubo, Wenbin Huang, Yaoran Sun, Xinhua Zhu, Julian Evans, Xian Song, Tongyu Geng, and Sailing He. 2023. "A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios" Applied Sciences 13, no. 9: 5361. https://doi.org/10.3390/app13095361

APA Style

Fu, X., Huang, W., Sun, Y., Zhu, X., Evans, J., Song, X., Geng, T., & He, S. (2023). A Novel Dataset for Multi-View Multi-Player Tracking in Soccer Scenarios. Applied Sciences, 13(9), 5361. https://doi.org/10.3390/app13095361

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop