驾驶员疲劳状态检测技术研究与工程实现
摘要
在频繁发生的道路交通事故中,驾驶员的疲劳驾驶往往是肇事的主要原因之一,因此对驾驶员的疲劳状态进行实时检测,减少由疲劳驾驶引发的交通事故,有着重要的现实意义。本文利用机器视觉技术对驾驶员人脸进行图像分析,提取多个有效的驾驶员疲劳特征信息,结合模糊逻辑与人工神经网络技术对驾驶员疲劳状态进行检测与预警,取得了较好的效果。本文的主要研究工作和贡献在于:
(1)对疲劳及驾驶疲劳的形成过程进行了分析,建立了基于人-车-环境的驾驶员疲劳分析系统。根据驾驶行为S-O-R理论,建立了驾驶行为过程模式,并在此基础上,建立了以人为中心的驾驶疲劳行为模式。
(2)从心理学、生理学及行为科学等角度对驾驶员疲劳的机理进行了分析。驾驶员操纵车辆的动作是驾驶行为,而驾驶员驾车时脑中所思、所想、所感等则是驾驶心理。研究表明,人体昼夜觉醒水平的变化规律与驾驶员疲劳引发的驾驶事故的时间分布是完全一致的。
(3)从驾驶时间,驾驶速度、驾驶环境、身体状况、道路状况等因素对驾驶疲劳的影响进行了分析,得出:随着驾驶时间增长,各个因素对驾驶员疲劳的影响加剧;中断后,具有可恢复性。
(4)提出了一种基于投影和分块复杂度的眼睛定位方法。首先采用由粗到细的两级定位策略,根据人脸图像的投影定出人眼的大致区域;然后将此区域分割成若干小块,找出复杂度最大的几个小块,建立判定规则,排除非眼睛的小块,剩下的两块即为左右眼睛。
(5)提出了基于颜色和纹理复合特征的双状态人眼跟踪算法。该算法的要点在于对计算所得的人眼睁开状态的权重附加了一个判定准则,即当本轮粒子更新时的最大粒子概率小于某个阈值P_(min)时认为该轮更新无效,在试验中我们取P_(min)=0.65,取得了较好的跟踪效果。
(6)提出了基于Gabor小波滤波器的人眼纹理特征提取的计算方法。通过将Gabor小波滤波器提取的人眼纹理特征向量送入RBF神经网络进行学习、分类,输出值设定为0,1,2,3,4,分别对应眼睛五种闭合程度状态0%,25%,50%,75%,100%,可以简单方便的计算参数PERCLOS和AECS的值。
(7)通过对多种人脸识别方法进行比较分析,提出采用Hopfield神经网络进行驾驶员疲劳检测时的人脸识别方法。
(8)针对实时驾驶员疲劳状态检测的要求,建立了以DM642为核心处理器的实时图像采集、处理硬件系统。利用模糊神经网络技术,将驾驶员疲劳视觉特征参数进行有机融合,设计了新的驾驶员疲劳状态检测方法。
本文的主要创新点如下:
(1)提出了一种基于投影和分块复杂度的眼睛定位方法。。
(2)提出了基于颜色和纹理复合特征的双状态人眼跟踪算法
(3)提出了基于Gabor小波滤波器的人眼纹理特征提取方法,将特征向量通过RBF神经网络进行分类输出,可以方便地计算PERCLOS和AECS的值。
(4)提出了将模糊神经网络技术应用到驾驶员疲劳实时检测系统中的新的疲劳检测方法。首先利用计算机视觉技术对在行车过程中的驾驶员表情变化、眨眼变化、眼动变化及视线变化等进行监控,从监控所得图像数据中提取PERCLOS、AECS、NodFreq、YawnFreq等四个疲劳特征参数,通过模糊神经网络将这四个疲劳特征参数信息进行融合,将检测出来的特征值与PVT量化值对比评价,以确定驾驶员的疲劳级别,从而采取相应的报警级别。实验表明,这个系统在驾驶员疲劳实时检测中具有较好的效果。
It is well known that driver fatigue is one of main causes of the traffic accident. Thus,the real-time detection of the driver fatigue has the vital practical significance in reducing the accidents caused by driver fatigue.Some beneficial improvement have been achieved in this field by employing the machine vision technology to analysis the facial image,then extracting many effective driver fatigue characters from these informations,and finally using the fuzzy logic and the artificial neural networks technology to detect and provide the early warning about the state of fatigue.The main task and contribution of this paper lie in:
(1) The fatigue and the process that causes the driving fatigue are analyzed.Then based on the person-vehicle-environment systems,the driver fatigue analysis system has been established.Furthermore,the model of the driver behavior is also proposed based on the S-O-R theory.With these preparations,the person-focused model is established to analyze the driver fatigue behavior.
(2) The mechanism of driver fatigue is studied from a psychological,physiological and behavioral science point of view.The actions employed by the driver to control the vehicles are named driver behavior.And the thought and feeling of the driver during the driving is called the driver psychology.It is proved that the time distribution of the traffic accident caused by the driver fatigue is just the same as the level of the sleeping or the wake of human beings.
(3) We mainly study the influence of the following factors such as the driving time, speeding,driving environment,the background of the driving,social environment, health,road conditions etc.,to the driving fatigue.It is proved that the influence of these factors to the driver fatigue is greatly augmented as the driving time increases.However, such an influence can be resumed if the driving is stopped.
(4) A new algorithm is proposed to locate the eyes,which is based on the projection and the intersected complexity.Specifically,a coarse-to-fine two level localization strategy is first adopted to approximately locate the area of the eyes based on the projection of the face image.Then,this area is divided into several small regions and some of them with high complexity are extracted.Finally,the regions that do not correspond to eyes are excluded by using the judging rules,and the rest is just in relation to the eyes.
(5) Based on the color and texture compound characterization,the double state eye's tracking algorithm is proposed.The main idea of this algorithm lies in adding an additional determination criterion to the weight which denotes the open degree of the human eyes.More specifically,if the probability of the biggest granule is smaller than a given threshold P_(min) as the granule updates in the epicycle.We take P_(min)=0.65 on trial and achieved good tracking effect.
(6) Based on the Gabor wave filterings,an algorithm is proposed to extract the texture of eyes.The obtained characteristics is imported into RBF neural network to learn and its output is classified into five types as 0,1,2,3,4 which correspond to the value of the parameters PERCLOS and AECS 0%,25%,50%,75%,100%,respectively.
(7) Many of face detection and identification methods are studied.Firstly we introduce many face detection and face recognition method.Then the Hopfield neural network is adoptted to carry out the facial recognition.The experimental results illustrate that such a recognition method has higher identification accuracy in small sample set than BP neural networks.
(8) An experimental platform is estabilished to real time detect the fatigue of the driver,in which the DM642 is the main processor.By employing the fuzzy neural network technique,the parameters that denote the degree of the driver fatigue are fused and then a new algorithm is presented to detect the driver fatigue.
The main contributions of this paper are as follows:
(1) A new algorithme,which is based on the projection and the intersected complexity,is proposed to locate the eyes.
(2) A double state eye's tracking algorithm which is based on the color and texture compound characterization is proposed.
(3) An algorithm based on the Gabor wave filterings is proposed to extract the texture of eyes,by which the PERCLOS and ECS can be obtained through the output of the RBF neural network.
(4) Fuzzy neural network is introduced to detect the driver fatigue.The computer vision technology is first used to collect the changes of the driver's expression,blinks, movement of the eyes,and the line of sight etc.And then the four fatigue characteristic parameters named PERCLOS,AECS,NodFreq,and YawnFreq are extracted from these image informations.Finally,these obtained datas are fused by the fuzzy neural network and the output is just corresponded to the classification of the fatigue.Compared these value with the PVT,the degree of the driver fatigue is decided and then the corresponding warning degree is confirmed.The results of the experiment have indicated that our method is more efficient in real-time fatigue detection.Furthermore, the proposed algorithm is also valuable for the other related fields.
(1)对疲劳及驾驶疲劳的形成过程进行了分析,建立了基于人-车-环境的驾驶员疲劳分析系统。根据驾驶行为S-O-R理论,建立了驾驶行为过程模式,并在此基础上,建立了以人为中心的驾驶疲劳行为模式。
(2)从心理学、生理学及行为科学等角度对驾驶员疲劳的机理进行了分析。驾驶员操纵车辆的动作是驾驶行为,而驾驶员驾车时脑中所思、所想、所感等则是驾驶心理。研究表明,人体昼夜觉醒水平的变化规律与驾驶员疲劳引发的驾驶事故的时间分布是完全一致的。
(3)从驾驶时间,驾驶速度、驾驶环境、身体状况、道路状况等因素对驾驶疲劳的影响进行了分析,得出:随着驾驶时间增长,各个因素对驾驶员疲劳的影响加剧;中断后,具有可恢复性。
(4)提出了一种基于投影和分块复杂度的眼睛定位方法。首先采用由粗到细的两级定位策略,根据人脸图像的投影定出人眼的大致区域;然后将此区域分割成若干小块,找出复杂度最大的几个小块,建立判定规则,排除非眼睛的小块,剩下的两块即为左右眼睛。
(5)提出了基于颜色和纹理复合特征的双状态人眼跟踪算法。该算法的要点在于对计算所得的人眼睁开状态的权重附加了一个判定准则,即当本轮粒子更新时的最大粒子概率小于某个阈值P_(min)时认为该轮更新无效,在试验中我们取P_(min)=0.65,取得了较好的跟踪效果。
(6)提出了基于Gabor小波滤波器的人眼纹理特征提取的计算方法。通过将Gabor小波滤波器提取的人眼纹理特征向量送入RBF神经网络进行学习、分类,输出值设定为0,1,2,3,4,分别对应眼睛五种闭合程度状态0%,25%,50%,75%,100%,可以简单方便的计算参数PERCLOS和AECS的值。
(7)通过对多种人脸识别方法进行比较分析,提出采用Hopfield神经网络进行驾驶员疲劳检测时的人脸识别方法。
(8)针对实时驾驶员疲劳状态检测的要求,建立了以DM642为核心处理器的实时图像采集、处理硬件系统。利用模糊神经网络技术,将驾驶员疲劳视觉特征参数进行有机融合,设计了新的驾驶员疲劳状态检测方法。
本文的主要创新点如下:
(1)提出了一种基于投影和分块复杂度的眼睛定位方法。。
(2)提出了基于颜色和纹理复合特征的双状态人眼跟踪算法
(3)提出了基于Gabor小波滤波器的人眼纹理特征提取方法,将特征向量通过RBF神经网络进行分类输出,可以方便地计算PERCLOS和AECS的值。
(4)提出了将模糊神经网络技术应用到驾驶员疲劳实时检测系统中的新的疲劳检测方法。首先利用计算机视觉技术对在行车过程中的驾驶员表情变化、眨眼变化、眼动变化及视线变化等进行监控,从监控所得图像数据中提取PERCLOS、AECS、NodFreq、YawnFreq等四个疲劳特征参数,通过模糊神经网络将这四个疲劳特征参数信息进行融合,将检测出来的特征值与PVT量化值对比评价,以确定驾驶员的疲劳级别,从而采取相应的报警级别。实验表明,这个系统在驾驶员疲劳实时检测中具有较好的效果。
It is well known that driver fatigue is one of main causes of the traffic accident. Thus,the real-time detection of the driver fatigue has the vital practical significance in reducing the accidents caused by driver fatigue.Some beneficial improvement have been achieved in this field by employing the machine vision technology to analysis the facial image,then extracting many effective driver fatigue characters from these informations,and finally using the fuzzy logic and the artificial neural networks technology to detect and provide the early warning about the state of fatigue.The main task and contribution of this paper lie in:
(1) The fatigue and the process that causes the driving fatigue are analyzed.Then based on the person-vehicle-environment systems,the driver fatigue analysis system has been established.Furthermore,the model of the driver behavior is also proposed based on the S-O-R theory.With these preparations,the person-focused model is established to analyze the driver fatigue behavior.
(2) The mechanism of driver fatigue is studied from a psychological,physiological and behavioral science point of view.The actions employed by the driver to control the vehicles are named driver behavior.And the thought and feeling of the driver during the driving is called the driver psychology.It is proved that the time distribution of the traffic accident caused by the driver fatigue is just the same as the level of the sleeping or the wake of human beings.
(3) We mainly study the influence of the following factors such as the driving time, speeding,driving environment,the background of the driving,social environment, health,road conditions etc.,to the driving fatigue.It is proved that the influence of these factors to the driver fatigue is greatly augmented as the driving time increases.However, such an influence can be resumed if the driving is stopped.
(4) A new algorithm is proposed to locate the eyes,which is based on the projection and the intersected complexity.Specifically,a coarse-to-fine two level localization strategy is first adopted to approximately locate the area of the eyes based on the projection of the face image.Then,this area is divided into several small regions and some of them with high complexity are extracted.Finally,the regions that do not correspond to eyes are excluded by using the judging rules,and the rest is just in relation to the eyes.
(5) Based on the color and texture compound characterization,the double state eye's tracking algorithm is proposed.The main idea of this algorithm lies in adding an additional determination criterion to the weight which denotes the open degree of the human eyes.More specifically,if the probability of the biggest granule is smaller than a given threshold P_(min) as the granule updates in the epicycle.We take P_(min)=0.65 on trial and achieved good tracking effect.
(6) Based on the Gabor wave filterings,an algorithm is proposed to extract the texture of eyes.The obtained characteristics is imported into RBF neural network to learn and its output is classified into five types as 0,1,2,3,4 which correspond to the value of the parameters PERCLOS and AECS 0%,25%,50%,75%,100%,respectively.
(7) Many of face detection and identification methods are studied.Firstly we introduce many face detection and face recognition method.Then the Hopfield neural network is adoptted to carry out the facial recognition.The experimental results illustrate that such a recognition method has higher identification accuracy in small sample set than BP neural networks.
(8) An experimental platform is estabilished to real time detect the fatigue of the driver,in which the DM642 is the main processor.By employing the fuzzy neural network technique,the parameters that denote the degree of the driver fatigue are fused and then a new algorithm is presented to detect the driver fatigue.
The main contributions of this paper are as follows:
(1) A new algorithme,which is based on the projection and the intersected complexity,is proposed to locate the eyes.
(2) A double state eye's tracking algorithm which is based on the color and texture compound characterization is proposed.
(3) An algorithm based on the Gabor wave filterings is proposed to extract the texture of eyes,by which the PERCLOS and ECS can be obtained through the output of the RBF neural network.
(4) Fuzzy neural network is introduced to detect the driver fatigue.The computer vision technology is first used to collect the changes of the driver's expression,blinks, movement of the eyes,and the line of sight etc.And then the four fatigue characteristic parameters named PERCLOS,AECS,NodFreq,and YawnFreq are extracted from these image informations.Finally,these obtained datas are fused by the fuzzy neural network and the output is just corresponded to the classification of the fatigue.Compared these value with the PVT,the degree of the driver fatigue is decided and then the corresponding warning degree is confirmed.The results of the experiment have indicated that our method is more efficient in real-time fatigue detection.Furthermore, the proposed algorithm is also valuable for the other related fields.
引文
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