【摘要】：在經(jīng)濟(jì)與科技發(fā)展的推動(dòng)作用下,越來(lái)越多的上班族以汽車作為交通工具,這樣既方便了出行又節(jié)省了時(shí)間。但是,道路上出現(xiàn)的事故伴隨著汽車的普遍應(yīng)用也出現(xiàn)了井噴式的增長(zhǎng)。根據(jù)調(diào)查研究與科學(xué)統(tǒng)計(jì),在發(fā)生交通事故的各種原因中,疲勞駕駛成為主要原因。盡管如此,由于檢測(cè)技術(shù)及軟件處理的不成熟與不完善,使得檢測(cè)駕駛員疲勞程度的產(chǎn)品還不能得到廣泛的引用,從而沒(méi)能減輕甚至杜絕類似交通事故的發(fā)生,造成了不可估量的生命與財(cái)產(chǎn)的損失。本文以此為背景,并對(duì)行車過(guò)程中出現(xiàn)疲勞狀況時(shí)的面部表現(xiàn)進(jìn)行觀察和分析,提取出疲勞面部信息,結(jié)合面部檢測(cè)及多特征處理技術(shù),設(shè)計(jì)了能檢測(cè)疲勞狀態(tài)的系統(tǒng),并在系統(tǒng)上完成了算法驗(yàn)證。此系統(tǒng)包括人臉檢測(cè)、疲勞特征提取和疲勞狀態(tài)判斷三個(gè)部分。其中,提取的面部特征有三個(gè):眼睛特征、頭部特征和嘴部特征。文中首先對(duì)系統(tǒng)的需求和工作流程進(jìn)行了分析,然后對(duì)各部分采用的算法進(jìn)行了論述。在檢測(cè)之初,本文先采用了Adaboost算法,在計(jì)算出人臉Haar特征之后用于人臉的檢測(cè)。在算法的使用中發(fā)現(xiàn),其速度和對(duì)傾斜時(shí)人臉的檢測(cè)有很多不足。因此,在后期引入了KCF跟蹤算法,并把這兩者進(jìn)行結(jié)合,在應(yīng)用對(duì)比后發(fā)現(xiàn),此改進(jìn)無(wú)論在時(shí)間上還是對(duì)傾斜人臉的檢測(cè)上,都有相當(dāng)大的提升。然后,論述了眼睛特征提取、頭部特征提取和嘴部特征提取的方法:在提取眼睛狀態(tài)的時(shí)候,首先根據(jù)先驗(yàn)知識(shí)確定眼睛子窗口,進(jìn)行二值化處理之后利用灰度積分投影分別提取出單個(gè)眼睛窗口,并求出各眼睛區(qū)域的最小的外接矩形,以矩形面積大小確定眼睛狀態(tài);提取嘴部狀態(tài)時(shí),根據(jù)先驗(yàn)知識(shí)提取出鼻孔和嘴部區(qū)域,并針對(duì)傳統(tǒng)Canny邊緣檢測(cè)存在的不足采用自適應(yīng)邊緣檢測(cè)算法得到鼻孔和嘴部輪廓,根據(jù)鼻孔到上下嘴唇間距離的比值確定出嘴巴的狀態(tài);對(duì)頭部特征的確定則是根據(jù)人臉矩形框坐標(biāo)位置的變化得出的。在對(duì)疲勞特征提取算法進(jìn)行論述之后,介紹了疲勞狀態(tài)判定的方法,針對(duì)利用單特征進(jìn)行疲勞檢測(cè)存在的不足,提出融合眼睛特征、嘴部特征和頭部特征進(jìn)行檢測(cè)的方法,該方法提高了檢測(cè)的正確率。本文在實(shí)驗(yàn)室通過(guò)VS開(kāi)發(fā)環(huán)境設(shè)計(jì)了疲勞駕駛檢測(cè)系統(tǒng),并在此系統(tǒng)上分別對(duì)采用單特征的疲勞檢測(cè)和多特征融合的疲勞檢測(cè)方法進(jìn)行實(shí)驗(yàn)驗(yàn)證,結(jié)果表明:本文提出的融合多特征的疲勞檢測(cè)方法在準(zhǔn)確率上有了明顯的提高。
[Abstract]:Driven by the development of economy and technology, more and more commuters use cars as means of transportation, which not only facilitates travel but also saves time. However, accidents on the road accompanied by the widespread use of cars also appeared blowout growth. According to investigation and scientific statistics, fatigue driving is the main cause of traffic accidents. However, due to the immaturity and imperfection of detection technology and software processing, the products used to detect drivers' fatigue degree can not be widely used, which can not reduce or even eliminate the occurrence of similar traffic accidents. Resulting in incalculable loss of life and property. Based on this background, this paper observed and analyzed the facial performance of fatigue state in the course of driving, extracted the fatigue facial information, combined with facial detection and multi-feature processing technology, designed a system to detect fatigue state. The algorithm is verified on the system. The system includes face detection, fatigue feature extraction and fatigue state judgment. Among them, there are three facial features extracted: eye features, head features and mouth features. In this paper, the requirements and workflow of the system are analyzed firstly, and then the algorithms used in each part are discussed. At the beginning of detection, Adaboost algorithm is used to detect face after Haar feature is calculated. In the use of the algorithm, it is found that there are many shortcomings in the speed and face detection of tilt. Therefore, the KCF tracking algorithm is introduced in the later period, and the two algorithms are combined. After application comparison, it is found that the improvement has a considerable improvement in both the time and the tilt face detection. Then, the methods of eye feature extraction, head feature extraction and mouth feature extraction are discussed. After binary processing, the single eye window is extracted by gray integral projection, and the smallest external rectangle of each eye region is obtained. The state of the eye is determined by the size of the rectangle area, and the state of the mouth is extracted. According to the prior knowledge, the nostril and mouth regions are extracted, and the adaptive edge detection algorithm is adopted to get the nostril and mouth contours according to the shortcomings of traditional Canny edge detection, and the state of the mouth is determined according to the ratio of the distance between the nostrils and the upper and lower lips. The determination of the head feature is based on the change of the coordinate position of the face rectangular frame. After discussing the algorithm of fatigue feature extraction, the method of fatigue state determination is introduced. Aiming at the shortcomings of fatigue detection using single feature, the method of combining eye feature, mouth feature and head feature is put forward. This method improves the accuracy of detection. In this paper, the fatigue driving detection system is designed in the lab by using vs development environment, and the fatigue detection method using single feature and multi-feature fusion is verified by experiments on this system. The results show that the proposed fatigue detection method with multiple features has a better accuracy.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U463.6;U495;TP391.41

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