Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164796
Title: Multi-stream spatiotemporal networks for driver fatigue detection from infrared and depth videos
Authors: Ma, Xiaoxi
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: 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
Abstract: 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.
URI: https://hdl.handle.net/10356/164796
DOI: 10.32657/10356/164796
Schools: School of Electrical and Electronic Engineering 
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: embargo_20250217
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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  Until 2025-02-17
4.17 MBAdobe PDFUnder embargo until Feb 17, 2025

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