本文是计算机专业的留学生作业代写范例，题目是“A Survey "Fatigue Monitoring System"（调查“疲劳监测系统”）”，这是一个在智能手机上实现的疲劳检测系统。该系统将检测司机在驾驶时的睡意。如果幻想被系统检测到，它就会发出警报。这里的参数是眨眼、打哈欠和摇头。摇头是该系统采用的新参数。
Abstract 摘要
This is a fatigue detection system implemented in smartphone. This system will detect the drowsiness of the driver while he is driving. And if the reverie is detected by the system then it will generate an alert. In this the parameters are blinking of eyes, mouth yawning and head shaking. Head shaking is the new parameter which is used in this system.
These parameters are calculated to identify driver’s weariness. The Canny Active Contour method is used for detecting yawning and the Harr-like technique is used for detecting face and eye blinking.
通过计算这些参数来识别驾驶员的疲劳程度。用Canny主动轮廓法检测打哈欠，用Harr-like技术检测人脸和眨眼。
1.INTRODUCTION引言
Now a days because of the reverie and fatigue of the drivers daily the huge amount of accidents are occurs which becomes the reason of deaths, injuries, etc. For preventing accidents it is essential to monitor the fatigue and vigilance level.
现在的一天，由于司机每天的幻想和疲劳，大量的事故发生，这成为死亡，受伤等原因。为了预防事故的发生，必须对疲劳和警戒水平进行监测。
With the help of various active safety systems we can monitor the fatigue of driver, drowsiness of the driver, traffic on the roads, the vehicle and the F providing alerts to the driver. Using a statistically anthropometric face model the important facial points are automatically detected [2007]. Many researchers have focused on drivers behavioral measures using various techniques such as visual based detection and Physiological detection.
For face detection the Adaboost algorithm is used [2016]. Optic nurve fatigue correspondingly causes the eye blinking when the driver goes in state of reverie.
Fatigue and distraction detection can be observed by head orientation and head shaking is used only for fatigue detection.
To detect the face the Gravity Center Template method is used. Also for detecting the mouth corner Gray Protection and Gabor Wavelets technique is used. LDA technique is used for yawning. For detecting the lips motion features Spatial Fuzzy C-means clustering (s-FCM) method is used.
Also for the face detection skin color & texture are used. For identifying color shape we can use the color values like RGB, YCbCR and HSV and so on. Tracking algorithm is used to Face detection. Based on face detection Kalman Filter Motion Tracking algorithm is used.
2.A DROWSINESS AND POINT OF ATTENTION MONITORING SYSTEM FOR DRIVER VIGILANCE 一种用于司机警觉性的困倦和注意力监测系统
Jorge Batista [1] proposed a system which represents a framework which combines a robust feature location of face with face modeling having elliptical shape to measure the drowsiness and fatigue of driver in 2007. The solution works with the two parameters that are computation of eyelid movements and attention of head point.
Jorge Batista[1]在2007年提出了一个系统，该系统代表了一个框架，将人脸的鲁棒特征位置与具有椭圆形状的人脸建模相结合，以测量驾驶员的困倦和疲劳。该方法以眼睑运动的计算和头部点的注意力两个参数为求解条件。
By using statistically anthropometric face model it detect the facial points automatically. It is one of the advantage in this paper. The measures the features of human face, it is calculated by anthropometry models and it deals with the biological area.
But the disadvantage of this system is that the parameters used for detecting the vigilance level of driver are not sufficient, the parameters such as yawning should be considered for detecting the drowsiness.
3.DRIVER DROWSINESS MONITORING BASED ON YAWNING DETECTION 基于打哈欠检测的驾驶员嗜睡监测
Shabnam Abtahi, Behnoosh Hariri, Shervin Shirmohammadi [2] proposed a system for monitoring and detecting driver’s drowsiness in 2011. For identifying drivers fatigue and drowsiness it can detect the various techniques like yawning, eye tiredness, eye movement, face tracking and drowsiness monitoring. The use of existing systems that monitor a vigilance level of drivers is important to prevent road accidents. Some of the main warning signs that can be measured or identified as indications of driver fatigue are : daydreaming while on the road driving over the center line, yawning , feeling impatient, feeling reacting slowly, heavy eyes, sleepy face, blinking of eyes and motion of lips.
Shabnam Abtahi, Behnoosh Hariri, Shervin Shirmohammadi[2]在2011年提出了一个监测和检测司机睡意的系统。为了识别司机的疲劳和困倦，它可以检测各种技术，如打哈欠、眼疲劳、眼动、面部跟踪和困倦监测。使用现有的系统来监测司机的警惕性水平对于预防交通事故很重要。一些可以被测量或识别为司机疲劳迹象的主要警告信号有:在道路上驾驶越过中线时做白日梦、打哈欠、不耐烦、感觉反应缓慢、眼睛沉重、面部困倦、眨眼和嘴唇运动。
For the purpose of the detection of the face region using the difference among two images. Drivers yawn is then tracked on the bases of the distance between the midpoint of nostrils and the chin uses Gravity-Center template to track the face. Detection of mouth corners we can use grey projection and Gabor wavelets.
Finally to detect yawning LDA is applied to classify feature vectors. Then, through spatial fuzzy c-means (s-FCM) clustering a mouth window is extracted from the face region, in which lips are tracked.
The advantage of the existing system is that yawning detection system is newly included which is not present in previous system. Also it recognizes face by using the face color and texture. The color shape can be recognize by RGB, YCbCR and HSV [2].
4.A METHOD OF DETECTING DRIVER DROWSINESS STATE BASED ON MULTI-FEATURES OF FACE一种基于人脸多特征的驾驶员疲劳状态检测方法
Ping Wang and Lin Shen [3] proposed a system to detect face region because of its high correct rate the AdaBoost algorithm is used in 2012. So then the final solution found is that the exact positions of driver’s eyes and mouth are placed depending upon their geometric features respectively.
2012年采用AdaBoost算法的[3]人脸区域检测正确率较高，提出了一种人脸区域检测系统。所以最终的解决方案是驾驶员的眼睛和嘴的精确位置分别取决于它们的几何特征。
Not only the technique of PATECP (Percentage And Time that Eyelids Cover the Pupils) and PATMIO (Percentage And Time that Mouth Is Open) but also the new judging rules and techniques are used to find out whether the driver is drowsy or not.
The actual tests with current driving videos represents that our technique of detecting drivers drowsiness is based on eye as well as mouth features makes the conditions of detecting the driver’s reverie state wider and most accurate.
Finally, in short in the format of summary this existing paper represents the working of facial reverie state, state tracking, formatting, region location and AdaBoost algorithm are used.
The advantage of this system is that it is well and high accurate system and without influence from light.
5.AN EFFICIENT SYSTEM TO IDENTIFY USER ATTENTIVENESS BASED ON FATIGUE DETECTION一种基于疲劳检测的高效用户注意力识别系统
Syed Imran Ali, Dr. Prashant Singh, Sameer Jain [4] proposed a user alertness identification system which is based on fatique detection in 2014. In this system the web camera continuously captures images of the subject. By using efficient image processing techniques it focuses on lips and eyes to monitor their behavior.
Syed Imran Ali, Dr. Prashant Singh, Sameer Jain[4]在2014年提出了一种基于疲劳检测的用户警觉性识别系统。在这个系统中，网络摄像机不断地捕捉被摄对象的图像。通过使用高效的图像处理技术，它聚焦在嘴唇和眼睛上，以监控它们的行为。
It firstly captures the image that is input RGB image and convert it into gray image. By using erosion and dilation techniques the gray image is converted into blur image. The Sobel edge detection filter algorithm is used to find the edges of blur image. After detecting the face, this image is cut into two halves eyes part and mouth part. Again the first halve i.e. eye part image is cut into two parts left eye part and right eye part. If the drowsiness is detected in these images then it generates alert.
The advantages of this system are that it works efficiently with even in the presence of different illumination sources background , also it is light weight and it requires less CPU execution time.
6.A SMARTPHONE-BASED DRIVER FATIGUE DETECTION USING FUSION OF MULTIPLE REAL-TIME FACIAL FEATURES一种基于智能手机的实时人脸特征融合驾驶员疲劳检测方法
Yantao Qiao, Kai Zeng, Lina Xu and Xiaoyu Yin [6] proposed a fatigue monitoring system which focuses on fusion of information, it is implemented and designed in smartphone. The driver’s fatigueness indicators are eye blinking, head nodding and yawning are detected. The face and eye blinks are detected by using Harr-like technique. And the mouth yawning is detected by using Canny Active Contour method.
乔彦涛、曾凯、徐琳、尹晓宇[6]提出了一种以信息融合为核心的疲劳监测系统，并在智能手机上进行了实现和设计。检测到司机眨眼、点头、打哈欠等疲劳指标。利用类似harr的技术检测人脸和眼睛的眨眼。并利用Canny活动轮廓法对嘴巴打哈欠进行了检测。
In this system the new parameter is added for detecting the fatigue is head nodding i.e. head shaking.
The main advantage of this system is that it uses smart phone for detecting the fatigue of driver for preventing him from accidents and does not require other equipments such as camera.
In this way we have surveyed few techniques which are used for driver fatigue detection. Some of the technique or algorithm uses single facial feature to detect the fatigue of driver and prevent from accidents. From above mentioned approaches the last approach is having addition features to detect fatigue of driver. We can use the various facial features to detect driver’s drowsiness such as eye detection, face detection, yawning, head shaking. This approach is more advantageous in sense that it uses a new concept that is head shaking for the detection of driver fatigue because of these it will easily found the drowsiness of a driver. This system is based on smart phone so there is no need of other equipment’s.
通过这种方法，我们对几种用于驾驶员疲劳检测的技术进行了研究。一些技术或算法使用单一的面部特征来检测驾驶员的疲劳，防止事故的发生。在上述方法的基础上，最后一种方法是增加特征来检测驾驶员的疲劳。我们可以利用各种面部特征来检测驾驶员是否有睡意，如眼睛检测、面部检测、打哈欠、摇头等。这种方法的优势在于它采用了一种新的概念，即摇头来检测驾驶员的疲劳，因为它很容易发现驾驶员的瞌睡。该系统基于智能手机，不需要其他设备的支持。
留学生作业相关专业范文素材资料，尽在本网，可以随时查阅参考。本站也提供多国留学生课程作业写作指导服务，如有需要可咨询本平台。