Estimating the Number of Soccer Players using Simulation-based Occlusion Handling

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

Estimating the number of soccer players is crucial in- formation for occupancy analysis and other monitoring ac- tivities in sports analysis. It depends on player detection in the field that should be independent of the environment and light conditions. Thermal cameras are therefore a bet- ter option over normal RGB cameras. Detection of non- occluded players is doable but precise estimation of number of the players in groups is hard to achieve. Here we pro- pose a novel method for estimating number of the players in groups using computer graphics and virtual simulations. Occlusion conditions are first classified by using distinctive set of features trained by a bagged tree classifier. Estima- tion of the number of players is then performed by maximum likelihood of probability density based approach to further classify the occluded players. The results show that the im- plemented strategy is capable of providing precise results even during occlusion conditions.
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Details

Estimating the number of soccer players is crucial in- formation for occupancy analysis and other monitoring ac- tivities in sports analysis. It depends on player detection in the field that should be independent of the environment and light conditions. Thermal cameras are therefore a bet- ter option over normal RGB cameras. Detection of non- occluded players is doable but precise estimation of number of the players in groups is hard to achieve. Here we pro- pose a novel method for estimating number of the players in groups using computer graphics and virtual simulations. Occlusion conditions are first classified by using distinctive set of features trained by a bagged tree classifier. Estima- tion of the number of players is then performed by maximum likelihood of probability density based approach to further classify the occluded players. The results show that the im- plemented strategy is capable of providing precise results even during occlusion conditions.
Original languageEnglish
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
PublisherIEEE
Publication date2018
Pages1937-1946
ISBN (Print)978-1-5386-6101-7
ISBN (Electronic)978-1-5386-6100-0
DOI
Publication statusPublished - 2018
Publication categoryResearch
Peer-reviewedYes
EventIEEE Conference on Computer Vision and Pattern Recognition, 2018 - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition, 2018
LandUnited States
BySalt Lake City
Periode18/06/201822/06/2018
ID: 274798322