NDLI: Open Dataset Recorded by Single Cameras for Multi-Player Tracking in Soccer Scenarios

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Open Dataset Recorded by Single Cameras for Multi-Player Tracking in Soccer Scenarios

Content Provider	MDPI
Author	Huang, Wenbin He, Sailing Sun, Yaoran Evans, Julian Song, Xian Geng, Tongyu Sun, Guanrong Fu, Xubo
Copyright Year	2022
Description	Multi-player action recognition for automatic analysis in sports is the subject of increasing attention. Trajectory-tracking technology is key for accurate recognition, but little research has focused on this aspect, especially for non-professional matches. Here, we study multi-player tracking in the most popular and complex sport among non-professionals—soccer. In this non-professional soccer player tracking (NPSPT) challenge, single-view-based motion recording systems for continuous data collection were installed in several soccer fields, and a new benchmark dataset was collected. The dataset consists of 17 2-min long super-high-resolution videos with diverse game types consistently labeled across time, covering almost all possible situations for multi-player detection and tracking in real games. A comprehensive evaluation was conducted on the state-of-the-art multi-object-Tracking (MOT) systems, revealing insights into player tracking in real games. Our challenge introduces a new dimension for researchers in the player recognition field and will be beneficial to further studies.
Starting Page	7473
e-ISSN	20763417
DOI	10.3390/app12157473
Journal	Applied Sciences
Issue Number	15
Volume Number	12
Language	English
Publisher	MDPI
Publisher Date	2022-07-25
Access Restriction	Open
Subject Keyword	Applied Sciences Multi-player Tracking Soccer Non-professional Mot Automatic
Content Type	Text
Resource Type	Article

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