摘要
目前疲劳预警算法多采用实时监测报警的方式,这在高速行驶中具有很大的安全隐患。鉴于人类疲劳状态的时序相关性,提出一种基于面部动作时空特征提取的预警算法。首先,构建加入空间变换结构的卷积神经网络,识别人脸区域,对脸部特征点进行检测标记;其次,建立时空特征提取网络,利用采集的人脸图像序列,对未来图像序列进行预测并输出;最后,在输出的图像序列中根据眼部、嘴部综合状态判断是否发出警告。实验结果表明,以15 fps的速率采集图像,预测未来2 s 30帧图像的方式下,该算法能以90%以上的准确率提前26帧(约1.5 s)预警,且提前15帧(1 s)预警的准确率达到97%。在我国高速公路平均100 km/h的车速下,相当于提前40 m预警,能进一步减少交通事故的发生。
At present,the fatigue early warning algorithm mostly adopts real-time monitoring and alarming,which has great security risks in the high-speed driving environment.In view of the temporal correlation of human fatigue state,this paper proposes an early warning algorithm based on spatiotemporal feature extraction of facial motion.Firstly,a convolutional neural network with spatial transformation structure is constructed to identify the face region and detect and mark the facial feature points.Secondly,a spatiotemporal feature extraction network is established,and the real-time acquired facial image feature sequence is used to predict and output the future image sequence.Finally,in the outputted image sequence,the comprehensive states of eyes and mouth are used to determine whether a fatigue warning is issued or not.Experimental results show that,under the condition that the image is acquired at 15 frames per second and the 30 frames in the future 2 seconds are predicted,the proposed algorithm can achieve the accuracy of more than 90%when issuing a fatigue warning 26 frames(about 1.5 seconds)in advance,and the accuracy of 97%when issuing a fatigue warning 15 frames(1 second)in advance.Under the average speed of 100 km/h in China's expressways,it is equivalent to an early warning of 40 meters in advance,which can further reduce the occurrence of traffic accidents.
作者
郁松
卢霖胤
YU Song;LU Lin-yin(School of Software Engineering,Central South University,Changsha 410083,China)
出处
《计算机工程与科学》
CSCD
北大核心
2019年第10期1763-1770,共8页
Computer Engineering & Science
基金
湖南省自然科学基金(S2018JJMSXM0177)
关键词
疲劳预警
深度学习
时空特征提取
状态预测
fatigue warning
deep learning
spatiotemporal feature extraction
state prediction
