We propose a condition-adaptive representation learning framework for the
driver drowsiness detection based on 3D-deep convolutional neural network. The
proposed framework consists of four models: spatio-temporal representation
learning, scene condition understanding, feature fusion, and drowsiness
detection. The spatio-temporal representation learning extracts features that
can describe motions and appearances in video simultaneously. The scene
condition understanding classifies the scene conditions related to various
conditions about the drivers and driving situations such as statuses of wearing
glasses, illumination condition of driving, and motion of facial elements such
as head, eye, and mouth. The feature fusion generates a condition-adaptive
representation using two features extracted from above models. The detection
model recognizes drivers drowsiness status using the condition-adaptive
representation. The condition-adaptive representation learning framework can
extract more discriminative features focusing on each scene condition than the
general representation so that the drowsiness detection method can provide more
accurate results for the various driving situations. The proposed framework is
evaluated with the NTHU Drowsy Driver Detection video dataset. The experimental
results show that our framework outperforms the existing drowsiness detection
methods based on visual analysis.

我们提出了一种基于 3D 深度卷积神经网络的驾驶员疲劳检测的条件自适应表示学习框架。该框架通过四个模型实现，包括时空特征表示、场景条件理解、特征融合和疲劳检测。实验结果显示，该框架优于现有的基于视觉分析的疲劳检测方法。