Crowd estimation using key‐point matching with support vector regression

Abstract The crowd behaviour understanding and density estimation are some of the fast‐growing fields in video surveillance. There are many techniques (detection and regression) that are used as the method of crowd analysis and estimation. In the present approach, SVR (support vector regression) is...

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Autores principales:	E.M.C.L Ekanayake, Yunqi Lei
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Photography TR1-1050 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/0590108273ae48ba83ad4b95eb10047d
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id	oai:doaj.org-article:0590108273ae48ba83ad4b95eb10047d
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spelling	oai:doaj.org-article:0590108273ae48ba83ad4b95eb10047d2021-11-29T03:38:16ZCrowd estimation using key‐point matching with support vector regression1751-96671751-965910.1049/ipr2.12300https://doaj.org/article/0590108273ae48ba83ad4b95eb10047d2021-12-01T00:00:00Zhttps://doi.org/10.1049/ipr2.12300https://doaj.org/toc/1751-9659https://doaj.org/toc/1751-9667Abstract The crowd behaviour understanding and density estimation are some of the fast‐growing fields in video surveillance. There are many techniques (detection and regression) that are used as the method of crowd analysis and estimation. In the present approach, SVR (support vector regression) is used as the basic analysis technique and the novel key‐point matching with SURF (speedup robust feature) is used as the feature extractor for moving objects in the video. The traditional linear regression methods used mainly key‐point as one of the statistical features instead of matching with consecutive frames, but we used the magnitude of the optical flow for foreground object extraction instead of inter‐frame difference. The combination of the optical flow of foreground objects and key‐point matching generates new features apart from conventional features such as areas and corners. In this new approach, key‐point pairing with linear regression is tested with the PETS2009 dataset, and performance is compared with the existing approaches.E.M.C.L EkanayakeYunqi LeiWileyarticlePhotographyTR1-1050Computer softwareQA76.75-76.765ENIET Image Processing, Vol 15, Iss 14, Pp 3551-3558 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photography TR1-1050 Computer software QA76.75-76.765
spellingShingle	Photography TR1-1050 Computer software QA76.75-76.765 E.M.C.L Ekanayake Yunqi Lei Crowd estimation using key‐point matching with support vector regression
description	Abstract The crowd behaviour understanding and density estimation are some of the fast‐growing fields in video surveillance. There are many techniques (detection and regression) that are used as the method of crowd analysis and estimation. In the present approach, SVR (support vector regression) is used as the basic analysis technique and the novel key‐point matching with SURF (speedup robust feature) is used as the feature extractor for moving objects in the video. The traditional linear regression methods used mainly key‐point as one of the statistical features instead of matching with consecutive frames, but we used the magnitude of the optical flow for foreground object extraction instead of inter‐frame difference. The combination of the optical flow of foreground objects and key‐point matching generates new features apart from conventional features such as areas and corners. In this new approach, key‐point pairing with linear regression is tested with the PETS2009 dataset, and performance is compared with the existing approaches.
format	article
author	E.M.C.L Ekanayake Yunqi Lei
author_facet	E.M.C.L Ekanayake Yunqi Lei
author_sort	E.M.C.L Ekanayake
title	Crowd estimation using key‐point matching with support vector regression
title_short	Crowd estimation using key‐point matching with support vector regression
title_full	Crowd estimation using key‐point matching with support vector regression
title_fullStr	Crowd estimation using key‐point matching with support vector regression
title_full_unstemmed	Crowd estimation using key‐point matching with support vector regression
title_sort	crowd estimation using key‐point matching with support vector regression
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/0590108273ae48ba83ad4b95eb10047d
work_keys_str_mv	AT emclekanayake crowdestimationusingkeypointmatchingwithsupportvectorregression AT yunqilei crowdestimationusingkeypointmatchingwithsupportvectorregression
_version_	1718407628057477120

Crowd estimation using key‐point matching with support vector regression

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