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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.contributor.editorBolzoni, Len_NZ
dc.date.accessioned2019-11-27T03:39:26Z
dc.date.available2018-08en_NZ
dc.date.available2019-11-27T03:39:26Z
dc.date.issued2018en_NZ
dc.identifier.citationMukhtar, A., Cree, M. J., Scott, J. B., & Streeter, L. (2018). Gait analysis of pedestrians with the aim of detecting disabled people. Applied Mechanics and Materials, 884, 105–112. https://doi.org/10.4028/www.scientific.net/AMM.884.105en
dc.identifier.issn1662-7482en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/13198
dc.description.abstractGait classification is an effective and non-intrusive method for human identification and it has received significant attention in the recent years due to its applications in visual surveillance and monitoring systems. We analyse gait signatures using spatio-temporal motion characteristics of a person to answer the question ``is there a discriminating feature in the gait signal that can help to categorise a disabled person from healthy?''. The procedure has three steps: detection of a pedestrian using YOLO followed by the silhouette extraction using the Gaussian Mixture Model (GMM). Finally, skeletonization from the silhouette image to estimate head and torso locations and their angles with the vertical axis. Furthermore, velocity and acceleration signals were recorded to look for accelerating behaviour of person walking with a limp. Manual segmentations shows that the gait signal has information about unusual walking patterns but existing pedestrian detectors lack accuracy in extracting an accurate gait signal due to localization errors.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherTrans Tech Publications
dc.rightsThis is an author’s accepted version of an article published in the journal: Applied Mechanics and Materials. © 2018 Trans Tech Publications.
dc.titleGait analysis of pedestrians with the aim of detecting disabled peopleen_NZ
dc.typeJournal Article
dc.identifier.doi10.4028/www.scientific.net/AMM.884.105en_NZ
dc.relation.isPartOfApplied Mechanics and Materialsen_NZ
pubs.begin-page105
pubs.elements-id226783
pubs.end-page112
pubs.publisher-urlhttps://www.scientific.net/AMM.884.105en_NZ
pubs.volume884en_NZ


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