Ski Fall Detection From Digital Images Using Deep Learning
| aut.pubs.state | with-academic | |
| aut.relation.conference | ICCCV 2022: The 5th International Conference on Control and Computer Vision | en_NZ |
| aut.relation.endpage | 78 | |
| aut.relation.startpage | 70 | |
| dark.contributor.author | Zhu, Y | en_NZ |
| dark.contributor.author | Yan, Wei Qi | en_NZ |
| dc.contributor.author | Zhu, Yulin | |
| dc.contributor.author | Yan, Wei Qi | |
| dc.date.accessioned | 2026-11-03T23:05:09Z | |
| dc.date.copyright | 2022 | en_NZ |
| dc.date.issued | 2022-11-09 | en_NZ |
| dc.description.abstract | In 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.citation | ICCCV '22: Proceedings of the 5th International Conference on Control and Computer Vision. August 2022. ISBN: 9781450397315 DOI: 10.1145/3561613 | |
| dc.identifier.doi | 10.1145/3561613.3561625 | en_NZ |
| dc.identifier.isbn | 978-1-4503-9731-5 | en_NZ |
| dc.identifier.uri | http://hdl.handle.net/10292/22308 | |
| dc.publisher | ACM | en_NZ |
| dc.relation.uri | https://dl.acm.org/doi/10.1145/3561613.3561625 | en_NZ |
| dc.rights | Copyright © 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.accessrights | OpenAccess | en_NZ |
| dc.subject | CNN | |
| dc.subject | Deep learning | |
| dc.subject | Fall detection | |
| dc.subject | Skiing | |
| dc.subject | Visual object detection | |
| dc.title | Ski Fall Detection From Digital Images Using Deep Learning | en_NZ |
| dc.type | Conference Contribution | |
| pubs.elements-id | 483027 | |
| 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 |
