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Published VersionCC BY V4.0, Open Access
Published VersionCC BY V4.0, Open
Journal article
Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks
Neural Computing and Applications, Vol.32(13), pp.9731-9743
2020
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, multi-step approaches. However, computationally expensive techniques that achieve superior results on action recognition benchmarks (e.g. 3D convolutions, optical flow extraction) create bottlenecks for real-time, safety-critical applications on mobile devices. Here, we show how depthwise separable 3D convolutions, combined with an early fusion of spatial and temporal information, can achieve a balance between high prediction accuracy and real-time inference requirements. In particular, increased accuracy is achieved when assessment requires motion information, for example, when sunglasses conceal the eyes. Further, a custom TensorFlow-based smartphone application shows the true impact of various approaches on inference times and demonstrates the effectiveness of real-time monitoring based on out-of-sample data to alert a drowsy driver. Our model is pre-trained on ImageNet and Kinetics and fine-tuned on a publicly available Driver Drowsiness Detection dataset. Fine-tuning on large naturalistic driving datasets could further improve accuracy to obtain robust in-vehicle performance. Overall, our research is a step towards practical deep learning applications, potentially preventing micro-sleeps and reducing road trauma.
Details
- Title
- Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks
- Authors
- Jasper S. Wijnands (Corresponding Author) - The University of MelbourneJason Thompson (Author) - The University of MelbourneKerry A. Nice (Author) - The University of MelbourneGideon D. P. A. Aschwanden (Author) - The University of MelbourneMark Stevenson - The University of Melbourne
- Publication details
- Neural Computing and Applications, Vol.32(13), pp.9731-9743
- Publisher
- Springer UK
- Date published
- 2020
- DOI
- 10.1007/s00521-019-04506-0
- ISSN
- 1433-3058; 0941-0643
- Copyright note
- Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Grants
- Organisation Unit
- Centre for Human Factors and Systems Science
- Language
- English
- Record Identifier
- 991184300102621
- Output Type
- Journal article
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