Journal article

Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks

Jasper S Wijnands, Jason Thompson, Kerry A Nice, Gideon DPA Aschwanden, Mark Stevenson

Neural Computing and Applications | Springer Science and Business Media LLC | Published : 2019

Abstract

Driver drowsiness increases crash risk, leading to substantial road trauma each year. Drowsiness detection methods have received considerable attention, but few studies have investigated the implementation of a detection approach on a mobile phone. Phone applications reduce the need for specialised hardware and hence, enable a cost-effective roll-out of the technology across the driving population. While it has been shown that three-dimensional (3D) operations are more suitable for spatiotemporal feature learning, current methods for drowsiness detection commonly use frame-based, multistep approaches. However, computationally expensive techniques that achieve superior results on action recog..

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Grants

Awarded by Australian Research Council


Awarded by University of Melbourne


Awarded by National Health and Medical Research Council (Australia) Fellowship


Awarded by Australian Research Council Discovery Early Career Research Award


Funding Acknowledgements

This work was supported by the Australian Research Council [Grant No. LP150100680]; QBE; and the Transport Accident Commission. Further, this research was undertaken using the LIEF HPC-GPGPU Facility hosted at the University of Melbourne [Grant No. LE170100200]. M.S. is supported by a National Health and Medical Research Council (Australia) Fellowship [Grant No. APP1136250]. J.T. is supported by an Australian Research Council Discovery Early Career Research Award [Grant No. DE180101411]. The authors would like to acknowledge Katherine Scully for her assistance with the literature review, and the anonymous reviewers for their valuable feedback, which helped improve the quality of the original manuscript.