Large-Scale Person Detection and Localization using Overhead Fisheye Cameras

Yang, L; Li, L; Xin, X; Sun, Y; Song, Q; Wang, W

Large-Scale Person Detection and Localization using Overhead Fisheye Cameras

Yang, L Li, L Xin, X Sun, Y Song, Q Wang, W

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2024, 00, pp. 19904-19914
Issue Date:: 2024-01-15

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Field	Value	Language
dc.contributor.author	Yang, L
dc.contributor.author	Li, L
dc.contributor.author	Xin, X
dc.contributor.author	Sun, Y
dc.contributor.author	Song, Q
dc.contributor.author	Wang, W
dc.date	2023-10-01
dc.date.accessioned	2024-05-13T04:24:40Z
dc.date.available	2024-05-13T04:24:40Z
dc.date.issued	2024-01-15
dc.identifier.citation	2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2024, 00, pp. 19904-19914
dc.identifier.isbn	979-8-3503-0719-1
dc.identifier.issn	1550-5499
dc.identifier.uri	http://hdl.handle.net/10453/178906
dc.description.abstract	Location determination finds wide applications in daily life Instead of existing efforts devoted to localizing tourist photos captured by perspective cameras in this article we focus on devising person positioning solutions using overhead fisheye cameras Such solutions are advantageous in large field of view FOV low cost anti occlusion and unaggressive work mode without the necessity of cameras carried by persons However related studies are quite scarce due to the paucity of data To stimulate research in this exciting area we present LOAF the first large scale overhead fisheye dataset for person detection and localization LOAF is built with many essential features e g i the data cover abundant diversities in scenes human pose density and location ii it contains currently the largest number of annotated pedestrian i e 457K bounding boxes with groundtruth location information iii the body boxes are labeled as radius aligned so as to fully address the positioning challenge To approach localization we build a fisheye person detection network which exploits the fisheye distortions by a rotation equivariant training strategy and predict radius aligned human boxes end to end Then the actual locations of the detected persons are calculated by a numerical solution on the fisheye model and camera altitude data Extensive experiments on LOAF validate the superiority of our fisheye detector w r t previous methods and show that our whole fisheye positioning solution is able to locate all persons in FOV with an accuracy of0 5 m within 0 1 s
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE/CVF International Conference on Computer Vision (ICCV)
dc.relation.ispartof	2023 IEEE/CVF International Conference on Computer Vision
dc.relation.isbasedon	10.1109/iccv51070.2023.01827
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Large-Scale Person Detection and Localization using Overhead Fisheye Cameras
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Paris, France
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-13T04:24:33Z
pubs.finish-date	2023-10-06
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-10-01
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Location determination finds wide applications in daily life Instead of existing efforts devoted to localizing tourist photos captured by perspective cameras in this article we focus on devising person positioning solutions using overhead fisheye cameras Such solutions are advantageous in large field of view FOV low cost anti occlusion and unaggressive work mode without the necessity of cameras carried by persons However related studies are quite scarce due to the paucity of data To stimulate research in this exciting area we present LOAF the first large scale overhead fisheye dataset for person detection and localization LOAF is built with many essential features e g i the data cover abundant diversities in scenes human pose density and location ii it contains currently the largest number of annotated pedestrian i e 457K bounding boxes with groundtruth location information iii the body boxes are labeled as radius aligned so as to fully address the positioning challenge To approach localization we build a fisheye person detection network which exploits the fisheye distortions by a rotation equivariant training strategy and predict radius aligned human boxes end to end Then the actual locations of the detected persons are calculated by a numerical solution on the fisheye model and camera altitude data Extensive experiments on LOAF validate the superiority of our fisheye detector w r t previous methods and show that our whole fisheye positioning solution is able to locate all persons in FOV with an accuracy of0 5 m within 0 1 s

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http://hdl.handle.net/10453/178906