RGB-D fall detection via deep residual convolutional LSTM networks

Abobakr, Ahmed, Hossny, Mohammed, Abdelkader, Hala and Nahavandi, Saeid 2018, RGB-D fall detection via deep residual convolutional LSTM networks, in DICTA 2018 : Digital Image Computing: Techniques and Applications, IEEE, Piscataway, N.J., doi: 10.1109/DICTA.2018.8615759.

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Title RGB-D fall detection via deep residual convolutional LSTM networks
Author(s) Abobakr, AhmedORCID iD for Abobakr, Ahmed orcid.org/0000-0002-6664-2335
Hossny, MohammedORCID iD for Hossny, Mohammed orcid.org/0000-0002-1593-6296
Abdelkader, Hala
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
Publication date 2018
Total pages 7
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
Kinect
RGB-D
fall detection
ConvNet
LSTM
ConvLSTM
ISBN 9781538666029
Language eng
DOI 10.1109/DICTA.2018.8615759
Field of Research 080106 Image Processing
080104 Computer Vision
080109 Pattern Recognition and Data Mining
080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 920502 Health Related to Ageing
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2018, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30122013

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