Long-term recurrent predictive model for intent prediction of pedestrians via inverse reinforcement learning

Saleh, Khaled, Hossny, Mohammed and Nahavandi, Saeid 2018, Long-term recurrent predictive model for intent prediction of pedestrians via inverse reinforcement learning, in DICTA 2018 : Digital Image Computing: Techniques and Applications (DICTA), IEEE, Piscataway, N.J., doi: 10.1109/DICTA.2018.8615854.

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Title Long-term recurrent predictive model for intent prediction of pedestrians via inverse reinforcement learning
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 Digital Image Computing: Techniques and Applications. International Conference (2018 : Canberra, A.C.T.)
Conference location Canberra, A.C.T.
Conference dates 2018/12/10 - 2018/12/13
Title of proceedings DICTA 2018 : Digital Image Computing: Techniques and Applications (DICTA)
Publication date 2018
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Engineering
ISBN 9781538666029
Language eng
DOI 10.1109/DICTA.2018.8615854
Field of Research 080101 Adaptive Agents and Intelligent Robotics
080108 Neural, Evolutionary and Fuzzy Computation
080104 Computer Vision
Socio Economic Objective 810104 Emerging Defence Technologies
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2018, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30122012

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
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