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dc.contributor.authorMukhtar, Amiren_NZ
dc.contributor.authorCree, Michael J.en_NZ
dc.contributor.authorScott, Jonathan B.en_NZ
dc.contributor.authorStreeter, Leeen_NZ
dc.coverage.spatialAuckland, New Zealanden_NZ
dc.date.accessioned2019-03-26T23:46:38Z
dc.date.available2018en_NZ
dc.date.available2019-03-26T23:46:38Z
dc.date.issued2018en_NZ
dc.identifier.citationMukhtar, A., Cree, M. J., Scott, J. B., & Streeter, L. V. (2018). Mobility aids detection using Convolution Neural Network (CNN). Presented at the International Conference on Image and Vision Computing New Zealand (IVCNZ), Auckland, New Zealand: IEEE. https://doi.org/10.1109/IVCNZ.2018.8634731en
dc.identifier.urihttps://hdl.handle.net/10289/12430
dc.description.abstractThe automated detection of disabled persons in surveillance videos to gain data for lobbying access for disabled persons is a largely unexplored application. We train You Only Look Once (YOLO) CNN on a custom database and achieve an accuracy of 92% for detecting disabled pedestrians in surveillance videos. A person is declared disabled if they are detected in the close proximity of a mobility aid. The detection outcome was further categorised into five classes of mobility aids and precision was calculated.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherIEEEen_NZ
dc.rightsThis is an author’s accepted version of an article published in the Proceedings of International Conference on Image and Vision Computing New Zealand (IVCNZ). © 2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.sourceInternational Conference on Image and Vision Computing New Zealand (IVCNZ)en_NZ
dc.subjectconvolutional neural network
dc.subjectmobility aids
dc.subjectcomputer vision
dc.subjectYOLO
dc.titleMobility aids detection using Convolution Neural Network (CNN)en_NZ
dc.typeConference Contribution
dc.identifier.doi10.1109/IVCNZ.2018.8634731en_NZ
pubs.elements-id235910
pubs.finish-date2018-12-21en_NZ
pubs.start-date2018-12-19en_NZ


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