Authors:
Xiao Cao
1
;
2
;
Wei Hu
2
and
Hui Liu
3
Affiliations:
1
Shanghai Electric Automation Group, 200023, Shanghai, China
;
2
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore
;
3
Cognitive System Lab, University of Bremen, 28359, Bremen, Germany
Keyword(s):
Computer Vision, Autonomous Driving, Driver Posture Estimation, Human-Machine Interaction.
Abstract:
Human-machine interaction, especially driver posture estimation is important to the development of autonomous driving, which can facilitate safe and smooth driving behaviours. Besides, it also contributes to ergonomics research and human-machine interaction design for automated vehicles. The existing studies have got great achievements in body estimation, hand pose estimation, and even face feature estimation thanks to the rapid development of deep learning approaches and the upgrade of hardware equipment. However, most existing models can only process body estimation or hand estimation separately, which will impede the research on driver-vehicle interaction in autonomous driving. This is because the driving process is highly dependent on the cooperation between the body and hands behaviours. In this study, five popular deep learning models, including Simple Faster R-CNN, RootNet, PoseNet, Yolo v3, and graph convolutional neural network, are combined through a cascade method to devel
op an integrated model which can estimate body and hand simultaneously during the driving process. The coordinate transform system is proposed to connect models in series. Experiment results demonstrate the proposed method can produce 2D and 3D reorganization of the human body and hands simultaneously with acceptable accuracy.
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