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Image-Based Driver Drowsiness Detection

机译：基于图像的驱动器嗜睡检测

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How to extract effective features of fatigue in images and videos is important for many applications. This paper introduces a face image descriptor that can be used for discriminating driver fatigue in static frames. In this method, first, each facial image in the sequence is represented by a pyramid whose levels are divided into non-overlapping blocks of the same size, and hybrid image descriptor are employed to extract features in all blocks. Then the obtained descriptor is filtered out using feature selection. Finally, non-linear Support Vector Machines is applied to predict the drowsiness state of the subject in the image. The proposed method was tested on the public dataset NTH Drowsy Driver Detection (NTHUDDD). This dataset includes a wide range of human subjects of different genders, poses, and illuminations in reallife fatigue conditions. Experimental results show the effectiveness of the proposed method. These results show that the proposed hand-crafted feature compare favorably with several approaches based on the use of deep Convolutional Neural Nets.
机译：如何在图像和视频中提取疲劳的有效特征对于许多应用来说都很重要。本文介绍了面部图像描述符，可用于静态帧中的驾驶员疲劳。在该方法中，首先，序列中的每个面部图像由金字塔表示，该金字塔划分为相同大小的非重叠块，并且采用混合图像描述符来提取所有块中的特征。然后使用特征选择过滤所获得的描述符。最后，应用非线性支持向量机以预测图像中的对象的嗜隙状态。在公共数据集Nth昏昏欲睡的驱动程序检测（Nthuddd）上测试了所提出的方法。该数据集包括各种不同性别，姿势和灯具的各种人类主体，在Reallife疲劳条件下的照明。实验结果表明了该方法的有效性。这些结果表明，拟议的手工制作的特征在于基于使用深卷积神经网络的几种方法比较。

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