一种基于卷积神经网络的哈欠检测算法

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 227-229.

一种基于卷积神经网络的哈欠检测算法

马素刚^1,2,赵琛²,孙韩林²,韩俊岗²

长安大学信息工程学院西安710064¹
西安邮电大学计算机学院西安710121²

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:马素刚(1982-),男,博士生,高级工程师,CCF会员,主要研究方向为机器学习与数据挖掘,E-mail:msg@xupt.edu.cn;赵琛(1993-),男,硕士生,主要研究方向为机器学习与数据挖掘,E-mail:zhaochen@126.com;孙韩林(1980-),男,博士,副教授,主要研究方向为复杂网络分析,E-mail:sunhanlin@xupt.edu.cn;韩俊岗(1943-),男,教授,CCF会员,博士生导师,主要研究方向为计算机体系结构,E-mail:hjg@xupt.edu.cn。
基金资助:
国家自然科学基金(61373116),陕西省自然科学基金(2016JM6048),陕西省教育厅专项科研计划项目(17JK0696)资助

Yawning Detection Algorithm Based on Convolutional Neural Network

MA Su-gang^1,2,ZHAO Chen²,SUN Han-lin²,HAN Jun-gang²

School of Information Engineering,Chang’an University,Xi’an 710064,China¹
School of Computer Science and Technology,Xi’an University of Posts and Telecommunications,Xi’an 710121,China²

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 哈欠检测可以用于对驾驶员的疲劳驾驶行为发出警告,从而减少交通事故的发生。提出了一种基于卷积神经网络的哈欠检测算法,可以把驾驶员的面部图片直接作为神经网络的输入,避免对面部图片进行复杂的显式特征提取。利用Softmax分类器对神经网络提取的特征进行分类,判断是否为打哈欠行为。该算法在YawDD数据集上取得了92.4%的哈欠检测准确率。与现有多个算法相比,所提算法具有检测准确率高、实现简单等优点。

关键词: Softmax分类器, YawDD数据集, 哈欠检测, 卷积神经网络, 权值共享

Abstract: Yawning detection can be used to warn drivers of fatigue driving behavior,thereby reducing traffic accidents.A yawning detection algorithm based on convolutional neural network was proposed.The driver’s facial image can be directly used as input for neural network,so as to avoid the complex explicit feature extraction of the facial image.The Softmax classifier is used to classify the features extracted from the neural network to determine whether the behavior is yawning or not.This algorithm achieves 92.4% accuracy in the YawDD dataset.Compared with other existing algorithms,the proposed method has the advantages of high detection accuracy and simpleimplementation.

Key words: Convolutional neural network, Softmax classifier, Weight sharing, Yawning detection, Yawning detection dataset

中图分类号:

TP183

马素刚, 赵琛, 孙韩林, 韩俊岗. 一种基于卷积神经网络的哈欠检测算法[J]. 计算机科学, 2018, 45(6A): 227-229. https://doi.org/

MA Su-gang, ZHAO Chen, SUN Han-lin, HAN Jun-gang. Yawning Detection Algorithm Based on Convolutional Neural Network[J]. Computer Science, 2018, 45(6A): 227-229. https://doi.org/

参考文献

[1]DWIVEDI K,BISWARANJAN K,SETHI A.Drowsy driver detection using representation learning[C]∥IEEE International Advance Computing Conference.IEEE,2014:995-999.
[2]ABTAHI S,SHIRMOHAMMADI S,HARIRI B,et al.A yaw- ning measurement method using embedded smart cameras[C]∥Instrumentation and Measurement Technology Conference.IEEE,2013:1605-1608.
[3]GALLUP A C,ELDAKAR O T.The thermoregulatory theory of yawning:what we know from over 5 years of research[J].Frontiers in Neuroscience,2013,6:188.
[4]OMIDYEGANEH M,SHIRMOHAMMADI S,ABTAHI S,et al.Yawning Detection Using Embedded Smart Cameras[J].IEEE Transactions on Instrumentation & Measurement,2016,65(3):1-13.
[5]谢国波,陈云华,张灵,等.基于嘴巴特征点曲线拟合的哈欠检测[J].计算机工程与科学,2014,36(4):731-736.
[6]ANITHA C,VENKATESHA M K,ADIGA B S.A Two Fold Expert System for Yawning Detection[J].Procedia Computer Science,2016,92:63-71.
[7]FAN X,YIN B C,SUN Y F.Yawning detection based on Gabor wavelets and LDA[J].Beijing Gongye Daxue Xuebao,2009,35(3):409-413,432.
[8]LI L,CHEN Y,LI Z.Yawning detection for monitoring driver fatigue based on two cameras[C]∥International IEEE Confe-rence on Intelligent Transportation Systems.IEEE Xplore,2009:1-6.

相关文章 15

[1]	周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026
[2]	李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023
[3]	陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121
[4]	朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153
[5]	檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064
[6]	张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036
[7]	戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105
[8]	刘月红, 牛少华, 神显豪. 基于卷积神经网络的虚拟现实视频帧内预测编码 Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network 计算机科学, 2022, 49(7): 127-131. https://doi.org/10.11896/jsjkx.211100179
[9]	徐鸣珂, 张帆. Head Fusion:一种提高语音情绪识别的准确性和鲁棒性的方法 Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition 计算机科学, 2022, 49(7): 132-141. https://doi.org/10.11896/jsjkx.210100085
[10]	金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190
[11]	张嘉淏, 刘峰, 齐佳音. 一种基于Bottleneck Transformer的轻量级微表情识别架构 Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer 计算机科学, 2022, 49(6A): 370-377. https://doi.org/10.11896/jsjkx.210500023
[12]	王建明, 陈响育, 杨自忠, 史晨阳, 张宇航, 钱正坤. 不同数据增强方法对模型识别精度的影响 Influence of Different Data Augmentation Methods on Model Recognition Accuracy 计算机科学, 2022, 49(6A): 418-423. https://doi.org/10.11896/jsjkx.210700210
[13]	孙洁琪, 李亚峰, 张文博, 刘鹏辉. 基于离散小波变换的双域特征融合深度卷积神经网络 Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation 计算机科学, 2022, 49(6A): 434-440. https://doi.org/10.11896/jsjkx.210900199
[14]	杨玥, 冯涛, 梁虹, 杨扬. 融合交叉注意力机制的图像任意风格迁移 Image Arbitrary Style Transfer via Criss-cross Attention 计算机科学, 2022, 49(6A): 345-352. https://doi.org/10.11896/jsjkx.210700236
[15]	杨健楠, 张帆. 一种结合双注意力机制和层次网络结构的细碎农作物分类方法 Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure 计算机科学, 2022, 49(6A): 353-357. https://doi.org/10.11896/jsjkx.210200169

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed