Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.