Early intent prediction of vulnerable road users from visual attributes using multi-task learning network

Saleh, Khaled, Hossny, Mohammed and Nahavandi, Saeid 2017, Early intent prediction of vulnerable road users from visual attributes using multi-task learning network, in SMC 2017: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, N.J., pp. 3367-3372, doi: 10.1109/SMC.2017.8123150.

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Title Early intent prediction of vulnerable road users from visual attributes using multi-task learning network
Author(s) Saleh, Khaled
Hossny, MohammedORCID iD for Hossny, Mohammed orcid.org/0000-0002-1593-6296
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name Systems, Man, and Cybernetics. International Conference (2017 : Banff, Canada)
Conference location Banff, Canada
Conference dates 2017/10/05 - 2017/10/08
Title of proceedings SMC 2017: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics
Publication date 2017
Start page 3367
End page 3372
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Cybernetics
Computer Science
Intent prediction
VRUs
deep learning
ConvNet and ADAS
ISBN 9781538616451
Language eng
DOI 10.1109/SMC.2017.8123150
Indigenous content off
Field of Research 080106 Image Processing
Socio Economic Objective 880109 Road Safety
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2017, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30111922

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