Real-time head pose estimation using multi-task deep neural network

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Driver inattention is one of the main causes of traffic accidents. To avoid such accidents, advanced driver assistance system that passively monitors the driver's activities is needed. In this paper, we present a novel method to estimate a head pose from a monocular camera. The proposed algorithm is based on multi-task learning deep neural network that uses a small grayscale image. The network jointly detects multi-view faces and estimates head pose even under poor environment conditions such as illumination change, vibration, large pose change, and occlusion. We also propose a multi-task learning method that does not bias on a specific task with different datasets. Moreover, in order to fertilize training dataset, we establish and release the RCVFace dataset that has accurate head poses. The proposed framework outperforms state-of-the-art approaches quantitatively and qualitatively with an average head pose mean error of less than 4 in real-time. The algorithm applies to driver monitoring system that is crucial for driver safety. (C) 2018 Published by Elsevier B.V.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2018-05
Language
English
Article Type
Article
Keywords

FACE ALIGNMENT; MODELS; LOCALIZATION; TRACKING

Citation

ROBOTICS AND AUTONOMOUS SYSTEMS, v.103, pp.1 - 12

ISSN
0921-8890
DOI
10.1016/j.robot.2018.01.005
URI
http://hdl.handle.net/10203/242240
Appears in Collection
EE-Journal Papers(저널논문)
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