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Ski Fall Detection From Digital Images Using Deep Learning

aut.pubs.statewith-academic
aut.relation.conferenceICCCV 2022: The 5th International Conference on Control and Computer Visionen_NZ
aut.relation.endpage78
aut.relation.startpage70
dark.contributor.authorZhu, Yen_NZ
dark.contributor.authorYan, Wei Qien_NZ
dc.contributor.authorZhu, Yulin
dc.contributor.authorYan, Wei Qi
dc.date.accessioned2026-11-03T23:05:09Z
dc.date.copyright2022en_NZ
dc.date.issued2022-11-09en_NZ
dc.description.abstractIn this paper, we explore how to take advantage of computer vision to assist ski resorts and monitor the safety of skiers on the tracks. In order to quickly detect any falls or injures, and provide first aid for injured people, we make use of archived ski videos, which are employed to explore the possibility of skiers fall detection. Throughout combinations of visual object detection with human pose detection by using deep learning methods. Our ultimate goal of this project is to provide a way for ski safety monitoring which has potential applications for physical training. Our contribution in this paper is to propose a fall detection method suitable for skiers based on visual object detection, we have obtained 0.94 mAP accuracy in preliminary tests.
dc.identifier.citationICCCV '22: Proceedings of the 5th International Conference on Control and Computer Vision. August 2022. ISBN: 9781450397315 DOI: 10.1145/3561613
dc.identifier.doi10.1145/3561613.3561625en_NZ
dc.identifier.isbn978-1-4503-9731-5en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/22308
dc.publisherACMen_NZ
dc.relation.urihttps://dl.acm.org/doi/10.1145/3561613.3561625en_NZ
dc.rightsCopyright © 2022 ACM. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Beginning January 2026, all ACM publications and related artifacts in the ACM Digital Library will be made open access.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectCNN
dc.subjectDeep learning
dc.subjectFall detection
dc.subjectSkiing
dc.subjectVisual object detection
dc.titleSki Fall Detection From Digital Images Using Deep Learningen_NZ
dc.typeConference Contribution
pubs.elements-id483027
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences

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