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.
Close

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 publicationThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
PublisherIEEE
Publication date2018
Pages1937-1946
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