TY - GEN
T1 - Convolutional three-stream network fusion for driver fatigue detection from infrared videos
AU - Ma, Xiaoxi
AU - Chau, Lap Pui
AU - Yap, Kim Hui
AU - Ping, Guiju
N1 - Publisher Copyright:
© 2019 IEEE
PY - 2019/5
Y1 - 2019/5
N2 - We propose a convolutional three-stream network architecture for driver fatigue detection from infrared videos that are available both in the daytime and in the night time. Specifically, the convolutional three-stream network architecture incorporates current-infrared-frame-based spatial information, optical-flows-based short-term temporal information of two consecutive infrared frames and optical flow-motion history image-based (OF-MHI-based) temporal information within the infrared video sequence. And then these three networks are fused at the last convolutional layer by 3D CNN. Besides, an estimation method to evaluate the current driver fatigue level is proposed based on the fatigue detection results from previous frames, which helps to generate alerts properly in real-life driving applications. We show that the proposed method achieves state-of-the-art performance, 94.68% accuracy, in our driver behavior dataset using the infrared data.
AB - We propose a convolutional three-stream network architecture for driver fatigue detection from infrared videos that are available both in the daytime and in the night time. Specifically, the convolutional three-stream network architecture incorporates current-infrared-frame-based spatial information, optical-flows-based short-term temporal information of two consecutive infrared frames and optical flow-motion history image-based (OF-MHI-based) temporal information within the infrared video sequence. And then these three networks are fused at the last convolutional layer by 3D CNN. Besides, an estimation method to evaluate the current driver fatigue level is proposed based on the fatigue detection results from previous frames, which helps to generate alerts properly in real-life driving applications. We show that the proposed method achieves state-of-the-art performance, 94.68% accuracy, in our driver behavior dataset using the infrared data.
UR - http://www.scopus.com/inward/record.url?scp=85066783811&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2019.8702447
DO - 10.1109/ISCAS.2019.8702447
M3 - Conference article published in proceeding or book
AN - SCOPUS:85066783811
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
Y2 - 26 May 2019 through 29 May 2019
ER -