發(fā)布時(shí)間：2018-07-01 11:10

  本文選題：疲勞檢測(cè) + YCbCr�。� 參考：《鄭州大學(xué)》2014年碩士論文

【摘要】：交通的快速發(fā)展，帶來了很多負(fù)面效應(yīng)，其中疲勞駕駛帶來的交通事故已經(jīng)成為一個(gè)重要的影響因素。近年來，汽車的設(shè)計(jì)越來越滿足人類對(duì)舒適度的要求，疲勞駕駛就更容易發(fā)生。為了減少這種事故的發(fā)生，找出一種準(zhǔn)確的檢測(cè)疲勞狀態(tài)并且及時(shí)有效的給駕駛員一個(gè)警惕成為了一個(gè)必要的手段。本文在保證駕駛舒適度的基礎(chǔ)上，采用非接觸式疲勞檢測(cè)算法檢測(cè)出疲勞狀態(tài)。具體的疲勞檢測(cè)算法分開來講，有以下幾部分構(gòu)成： 人臉檢測(cè)部分，在選取了合適的人臉庫基礎(chǔ)上，根據(jù)判斷膚色在色彩空間上的特征，選取了基于YCbCr膚色特征的人臉檢測(cè)算法。首先對(duì)膚色進(jìn)行預(yù)處理，然后對(duì)得到的處理進(jìn)行積分投影后最終分離出人臉區(qū)域。人眼檢測(cè)部分，采用自適應(yīng)邊緣特征提取的人眼定位檢測(cè)算法。首先對(duì)提取出人臉圖像進(jìn)行Robert算子自適應(yīng)邊緣特征提取，經(jīng)過求梯度、計(jì)算復(fù)雜度等的處理后，基本上可以判斷出人的眼睛位置。在此基礎(chǔ)上提出對(duì)人眼進(jìn)行二次特征提取，經(jīng)過再次計(jì)算梯度和復(fù)雜度處理，得到一個(gè)眼睛的定位結(jié)果。實(shí)驗(yàn)結(jié)果也證明了這種方法能夠很大程度上改善人眼檢測(cè)的檢測(cè)率，并且能夠提高檢測(cè)速度。人眼狀態(tài)判斷部分，本文采用國際上通用的PERCLOS狀態(tài)識(shí)別方法對(duì)人眼進(jìn)行判斷。通過計(jì)算眼睛面積的大小，與標(biāo)準(zhǔn)里眼睛睜開度比較判斷眼睛的睜閉狀態(tài)。 論文中的人臉檢測(cè)算法、人眼定位檢測(cè)算法以及疲勞判斷算法是在MATLAB圖像函數(shù)庫實(shí)現(xiàn)。實(shí)驗(yàn)證明改進(jìn)后的算法對(duì)人臉檢測(cè)和人眼檢測(cè)率都有了明顯的提高，并且在一定的程度上提高了實(shí)時(shí)性，，所以本文采用的算法是可行的，具有一定的實(shí)際意義。
[Abstract]:The rapid development of traffic has brought a lot of negative effects, among which the traffic accident caused by fatigue driving has become an important factor. In recent years, the design of cars more and more meet the requirements of human comfort, fatigue driving is more likely to occur. In order to reduce the occurrence of this kind of accident, it is necessary to find out an accurate detection of fatigue state and to alert the driver timely and effectively. In this paper, on the basis of ensuring driving comfort, non-contact fatigue detection algorithm is used to detect fatigue state. The specific fatigue detection algorithm is divided into the following parts: the face detection part, on the basis of selecting the appropriate face database, judging the color features in color space, A face detection algorithm based on YCbCr color feature is selected. Firstly, the skin color is pretreated, then the face region is separated by integral projection. In the part of human eye detection, adaptive edge feature extraction algorithm is used to detect human eye location. Firstly, the human face image is extracted by Robert operator adaptive edge feature extraction. After processing the gradient and computational complexity, we can basically judge the position of human eyes. On the basis of this, the second feature extraction of human eye is proposed. After calculating the gradient and complexity again, the location result of one eye is obtained. The experimental results also show that this method can greatly improve the detection rate of human eye detection and improve the detection speed. In the part of human eye state judgment, this paper uses the universal Percos state recognition method to judge the human eye. By calculating the size of the eye area, the open state of the eye is judged by comparing it with the standard degree of eye opening. The face detection algorithm, human eye location detection algorithm and fatigue judgment algorithm are realized in MATLAB image function library. The experimental results show that the improved algorithm improves the face detection rate and the human eye detection rate obviously, and improves the real-time performance to a certain extent, so the algorithm adopted in this paper is feasible and has certain practical significance.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U491.254;U463.6

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