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计算机系统应用:2018,27(10):177-182
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基于多面部特征融合的驾驶员疲劳检测算法
刘炜煌, 钱锦浩, 姚增伟, 焦新涛, 潘家辉
(华南师范大学 软件学院, 佛山 528225)
Driver Fatigue Detection Algorithm Based on Multi-Facial Feature Fusion
LIU Wei-Huang, QIAN Jin-Hao, YAO Zeng-Wei, JIAO Xin-Tao, PAN Jia-Hui
(School of Software, South China Normal University, Foshan 528225, China)
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投稿时间:2018-02-06    修订日期:2018-02-28
中文摘要: 本文将卷积神经网络(Convolutional Neural Network,CNN)应用到视频理解中,提出一种基于多面部特征融合的驾驶员疲劳检测算法.本文使用多任务级联卷积网络(Multi-Task Cascaded Convolutional Networks,MTCNN)定位驾驶员的嘴部、左眼,使用CNN从驾驶员嘴部、左眼图像中提取静态特征,结合CNN从嘴部、左眼光流图中提取动态特征进行训练分类.实验结果表明,该算法比只使用静态图像进行驾驶员疲劳检测效果更好,准确率达到87.4%,而且可以很好地区别在静态图像中很相似的打哈欠和讲话动作.
Abstract:In this study, Convolution Neural Network (CNN) is applied to video comprehension, and a driver fatigue detection algorithm based on multi-facial feature fusion is proposed. In the study, Multi-Task Cascaded Convolutional Neural Networks (MTCNN) is used to locate the driver's mouth and left eye. CNN is used to extract the static features from the driver's mouth and left-eye image, combined with the dynamic features that CNN extracted from the mouth and left eye optical flow to train for classification. The experimental results show that this algorithm with an accuracy rate of 87.4% is better than only use the static image for driver fatigue detection and it can well distinguish between yawning and speech actions that are similar in static images.
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基金项目:国家自然科学基金青年科学基金(61503143);广州市科技计划项目珠江科技新星科技创新人才专项(201710010038);广东省自然科学基金博士科研启动项目(2014A030310244)
引用文本:
刘炜煌,钱锦浩,姚增伟,焦新涛,潘家辉.基于多面部特征融合的驾驶员疲劳检测算法.计算机系统应用,2018,27(10):177-182
LIU Wei-Huang,QIAN Jin-Hao,YAO Zeng-Wei,JIAO Xin-Tao,PAN Jia-Hui.Driver Fatigue Detection Algorithm Based on Multi-Facial Feature Fusion.COMPUTER SYSTEMS APPLICATIONS,2018,27(10):177-182

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