【摘要】：自中國加入世界貿(mào)易組織以來,中國經(jīng)濟(jì)得到蓬勃發(fā)展,全國的基礎(chǔ)設(shè)施建設(shè)逐漸完善,人民生活水平得到很大提升,私家車的數(shù)量逐年遞增,從而造成交通事故頻繁發(fā)生,給人民的生命財(cái)產(chǎn)造成重大影響,嚴(yán)重影響了交通運(yùn)輸效率。因此,開發(fā)駕駛員的安全行為督導(dǎo)系統(tǒng)有助于加強(qiáng)駕駛?cè)藛T的安全意識并進(jìn)行安全駕駛操作,有極高的實(shí)用價(jià)值和社會意義。針對目前的基于計(jì)算機(jī)視覺的駕駛員安全行為輔助系統(tǒng)主要集中于駕駛員的精神狀態(tài)和疲勞駕駛方向,而行車是否安全主要取決于駕駛員的駕駛行為是否符合安全操作規(guī)程,因此,本文以駕駛員的駕駛行為本身為切入點(diǎn),對不安全的駕駛行為進(jìn)行檢測。本文主要的研究內(nèi)容如下所示:(1)從駕駛員開車過程中的具體行為入手,把駕駛員的手部動(dòng)作行為分成兩類:駕駛員的手部運(yùn)動(dòng)趨勢和手部在方向盤的位置。通過車輛的轉(zhuǎn)向信息和檔位信息來判斷駕駛員的手部運(yùn)動(dòng)趨勢,通過計(jì)算機(jī)視覺技術(shù)來定位駕駛員的手部位置。(2)利用橢圓檢測技術(shù)來定位采集圖像中的方向盤,從而得到感興趣區(qū)域,通過大量統(tǒng)計(jì)分析,建立高斯膚色模型,根據(jù)得到的高斯膚色模型計(jì)算圖像中像素點(diǎn)屬于膚色區(qū)域的概率,通過大津閾值分割把駕駛員的手部從背景中分離出來,為了使膚色檢測結(jié)果可以應(yīng)對不同的外部環(huán)境,提出了自適應(yīng)的高斯膚色模型。(3)根據(jù)采集得到的駕駛員行為特征,訓(xùn)練駕駛員行為分類決策樹來進(jìn)行駕駛行為識別,并通過大量樣本驗(yàn)證方法可行性。
[Abstract]:Since China's entry into the World Trade Organization (WTO), China's economy has developed vigorously, the national infrastructure has been gradually improved, the standard of living of the people has been greatly improved, and the number of private cars has increased year by year, resulting in frequent traffic accidents. The life and property of the people have a major impact, seriously affecting the efficiency of transportation. Therefore, it is of great practical value and social significance to develop the driver's safety behavior supervision system, which is helpful to strengthen the driver's safety consciousness and carry out the safe driving operation. At present, the driver safety behavior assistant system based on computer vision mainly focuses on the driver's mental state and fatigue driving direction, and whether the driving safety depends on whether the driver's driving behavior conforms to the safe operation rules or not. Therefore, in this paper, the driver's driving behavior itself as a starting point, unsafe driving behavior detection. The main contents of this paper are as follows: (1) starting with the driver's specific behavior during driving, the author divides the driver's hand movement behavior into two categories: the driver's hand movement trend and the position of the hand on the steering wheel. The steering information and gear information of the vehicle are used to judge the movement trend of the driver's hand, and the position of the driver's hand is located by the computer vision technology. (2) the steering wheel in the captured image is located by using the elliptical detection technology. In order to get the region of interest, through a lot of statistical analysis, the Gao Si skin color model is established. According to the obtained Gao Si skin color model, the probability of pixels belonging to the skin color region in the image is calculated. The driver's hand is separated from the background by the threshold segmentation of Otsu. In order to make the skin color detection results adapt to different external environments, an adaptive Gao Si skin color model is proposed. (3) according to the driver's behavior characteristics collected, the skin color model is proposed. Driver behavior classification decision tree is trained to recognize driving behavior, and a large number of samples are used to verify the feasibility of the method.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U463.6;TP391.41

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