Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks
| Autor: | Wijnands, Jasper S., Thompson, Jason, Nice, Kerry A., Aschwanden, Gideon D. P. A., Stevenson, Mark |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Neural Computing and Applications (2019) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/s00521-019-04506-0 |
| Popis: | 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. Comment: 13 pages, 2 figures, 'Online First' version. For associated mp4 files, see journal website |
| Databáze: | arXiv |
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