A pilot study on footprint posture classification of passengers in light rail public transport via deep convolutional neural networks

Nahavandi, Darius, Abobakr, Ahmed, Iskander, Julie and Hossny, Mohammed 2018, A pilot study on footprint posture classification of passengers in light rail public transport via deep convolutional neural networks, in ITSC 2018 : Proceedings of the 2018 IEEE Intelligent Transportation Systems Conference, Institute of Electrical and Electronics Engineers, Piscataway, N.J., pp. 2724-2728, doi: 10.1109/ITSC.2018.8569589.

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Title A pilot study on footprint posture classification of passengers in light rail public transport via deep convolutional neural networks
Author(s) Nahavandi, DariusORCID iD for Nahavandi, Darius orcid.org/0000-0002-5007-9584
Abobakr, AhmedORCID iD for Abobakr, Ahmed orcid.org/0000-0002-6664-2335
Iskander, JulieORCID iD for Iskander, Julie orcid.org/0000-0002-3426-4376
Hossny, MohammedORCID iD for Hossny, Mohammed orcid.org/0000-0002-1593-6296
Conference name IEEE Intelligent Transportation Systems Society. Conference (2018 : Maui, Hawaii)
Conference location Maui, Hawaii
Conference dates 2018/11/04 - 2018/11/07
Title of proceedings ITSC 2018 : Proceedings of the 2018 IEEE Intelligent Transportation Systems Conference
Editor(s) [Unknown]
Publication date 2018
Series IEEE Intelligent Transportation Systems Society Conference
Start page 2724
End page 2728
Total pages 5
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Keyword(s) Sensors
Science & Technology
Technology
Automation & Control Systems
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Transportation Science & Technology
Computer Science
Engineering
Transportation
ISBN 9781728103235
Language eng
DOI 10.1109/ITSC.2018.8569589
Field of Research 080106 Image Processing
080104 Computer Vision
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2018, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30118111

Document type: Conference Paper
Collection: Institute for Intelligent Systems Research and Innovation (IISRI)
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