Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-time monitoring of driver drowsiness on mobile platforms using 3D
  neural networks   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.

25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th