Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal dense net

Saleh, Khaled, Hossny, Mohammed and Nahavandi, Saeid 2019, Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal dense net, in ICRA 2019 : Proceedings of the IEEE International Conference on Robotics and Automation, IEEE, Piscataway, N.J., pp. 9704-9710, doi: 10.1109/ICRA.2019.8793991.

Attached Files
Name Description MIMEType Size Downloads

Title Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal dense net
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 Robotics and Automation. Conference (2019 : Montreal, Quebec)
Conference location Montreal, Quebec
Conference dates 20-24 May. 2019
Title of proceedings ICRA 2019 : Proceedings of the IEEE International Conference on Robotics and Automation
Publication date 2019
Start page 9704
End page 9710
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Automation & Control Systems
Robotics
APPEARANCE
ISBN 9781538660263
ISSN 1050-4729
Language eng
DOI 10.1109/ICRA.2019.8793991
Indigenous content off
Field of Research 080106 Image Processing
080104 Computer Vision
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30129897

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 18 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Tue, 17 Sep 2019, 12:08:39 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.