Multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos

This thesis presents new methods for incorporating multi-stream networks into the driver fatigue detection system. For the depth video-based method, a two-stream CNN architecture is proposed to incorporate spatial information of the current depth frame and temporal information of neighboring depth f...

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Main Author: Ma, Xiaoxi
Other Authors: Yap Kim Hui
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/164796
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1647962023-03-06T07:30:04Z Multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos Ma, Xiaoxi Yap Kim Hui School of Electrical and Electronic Engineering EKHYap@ntu.edu.sg Engineering::Computer science and engineering This thesis presents new methods for incorporating multi-stream networks into the driver fatigue detection system. For the depth video-based method, a two-stream CNN architecture is proposed to incorporate spatial information of the current depth frame and temporal information of neighboring depth frames which is represented by motion vectors. For the infrared video-based method, a convolutional three-stream network is proposed to incorporate current-infrared-frame-based spatial information, optical-flow-based short-term temporal information of two consecutive infrared frames, and optical flow-motion history image-based temporal information within the infrared video sequence. Doctor of Philosophy 2023-02-20T02:11:13Z 2023-02-20T02:11:13Z 2022 Thesis-Doctor of Philosophy Ma, X. (2022). Multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/164796 https://hdl.handle.net/10356/164796 10.32657/10356/164796 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Ma, Xiaoxi
Multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos
description This thesis presents new methods for incorporating multi-stream networks into the driver fatigue detection system. For the depth video-based method, a two-stream CNN architecture is proposed to incorporate spatial information of the current depth frame and temporal information of neighboring depth frames which is represented by motion vectors. For the infrared video-based method, a convolutional three-stream network is proposed to incorporate current-infrared-frame-based spatial information, optical-flow-based short-term temporal information of two consecutive infrared frames, and optical flow-motion history image-based temporal information within the infrared video sequence.
author2 Yap Kim Hui
author_facet Yap Kim Hui
Ma, Xiaoxi
format Thesis-Doctor of Philosophy
author Ma, Xiaoxi
author_sort Ma, Xiaoxi
title Multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos
title_short Multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos
title_full Multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos
title_fullStr Multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos
title_full_unstemmed Multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos
title_sort multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/164796
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