【摘要】：在2010年,世界的車輛總數(shù)已經(jīng)超過十億,其中美國2.4億輛、中國7800萬輛。伴隨車輛的快速增長隨之而來的是交通事故的增加。而研究表明其中大約20%的事故與疲勞駕駛有關(guān)。目前關(guān)于駕駛疲勞檢測的研究方法基本都是應(yīng)用視覺信號和腦電、心電、眼電等生理信號。由于其非接觸性,視覺檢測仍然是主流。但是視覺檢測目前的研究有一些不足,特別是測試者在復(fù)雜環(huán)境,例如戴眼鏡或者在光線變化較大,傳統(tǒng)的二值分割等方法準(zhǔn)確度將大打折扣。有文獻(xiàn)表明握力變化也是衡量駕駛疲勞的有效特征之一。因此本文提出將視覺信號和握力信號融合來檢測駕駛疲勞,實驗結(jié)果表明相對于單一視覺信號,融合之后的算法檢測更加準(zhǔn)確。本文主要工作如下:(1)模擬駕駛平臺和疲勞駕駛檢測系統(tǒng)的搭建。為了盡可能真實地模擬實際駕駛操作,保證疲勞檢測算法有效性。在硬件方面采用的模擬方向盤和腳踏板與真實的駕駛體驗幾乎相同,駕駛場景模擬系統(tǒng)的設(shè)計涵蓋各種天氣和路況。在此基礎(chǔ)上構(gòu)建攝像頭和握力檢測于一體的疲勞駕駛檢測系統(tǒng)。(2)經(jīng)壓力傳感器采集到的握力原始信號表征能力非常有限,首先要對原始的數(shù)據(jù)進(jìn)行轉(zhuǎn)換和濾波,其中濾波采用實時性更好的線性動力系統(tǒng)模型平滑方法。預(yù)處理之后在握力的時域特征中選擇方差作為握力特征。(3)在人臉定位中研究Cascade結(jié)構(gòu)的人臉檢測器算法,應(yīng)用基于Haar特征的Adaboost算法使得人臉定位更加準(zhǔn)確。所有的視覺處理算法包括Adaboost和改進(jìn)的主動形狀模型均在基于Visual Studio2010的OPENCV中實現(xiàn)。(4)在定位人臉之后,利用改進(jìn)的主動形狀模型來進(jìn)行人臉77個特征點(diǎn)的搜索。利用眼睛和嘴部的特征點(diǎn)的高寬比來定義眼睛閉合度和嘴巴張開度。最后分別在戴眼鏡和不戴眼鏡的測試者驗證了算法的精確性和魯棒性。(5)提出了基于人臉和握力特征融合的疲勞駕駛檢測算法。對于眼睛閉合度、嘴部張開度及握力方差三個特征輸入進(jìn)行模糊推理系統(tǒng)的設(shè)計。在滿足規(guī)則的交叉性和一致性方面做了大量的實驗去調(diào)整模糊規(guī)則。在MATLAB中和已有的算法進(jìn)行了對比仿真,證明了算法的有效性。
[Abstract]:In 2010, the world had more than one billion vehicles, including 240 million in the United States and 78 million in China. With the rapid growth of vehicles, traffic accidents increase. Studies show that about 20% of these accidents are related to fatigue driving. At present, the research methods of driving fatigue detection are based on the use of visual signals and physiological signals such as EEG, ECG and eye electricity. Because of its non-contact, visual detection is still the mainstream. However, there are some shortcomings in the current research of visual detection, especially in complex environments, such as wearing glasses or changing light, the accuracy of traditional binary segmentation methods will be greatly reduced. Some literatures show that the change of grip strength is also one of the effective characteristics of measuring driving fatigue. Therefore, this paper proposes the fusion of visual signal and grip force signal to detect driving fatigue. The experimental results show that the fusion algorithm is more accurate than the single visual signal. The main work of this paper is as follows: (1) the building of simulated driving platform and fatigue driving detection system. In order to simulate the actual driving operation as truthfully as possible and ensure the validity of fatigue detection algorithm. The simulated steering wheel and pedals used in the hardware are almost the same as the real driving experience. The driving scene simulation system is designed to cover all kinds of weather and road conditions. On this basis, a fatigue driving detection system with camera and grip force detection is constructed. (2) the original signal of grip strength collected by pressure sensor is very limited. First, the original data must be converted and filtered. Among them, filtering adopts linear dynamic system model smoothing method which is better in real time. After preprocessing, the variance is selected as the grip force feature in the time domain feature of grip force. (3) face detector algorithm based on Cascade structure is studied in face location, and Adaboost algorithm based on Haar feature is used to make face location more accurate. All the visual processing algorithms, including Adaboost and the improved active shape model, are implemented in OPENCV based on Visual Studio2010. (4) after locating the face, the improved active shape model is used to search the 77 feature points of the face. Eye closure and mouth opening are defined by the aspect ratio of the feature points of the eye and mouth. Finally, the accuracy and robustness of the algorithm are verified by the testers with and without glasses. (5) A fatigue driving detection algorithm based on face and grip feature fusion is proposed. The fuzzy inference system is designed for eye closure, mouth opening and grip force variance. A lot of experiments have been done to adjust the fuzzy rules in the aspect of meeting the intersection and consistency of the rules. The effectiveness of the algorithm is proved by comparing and simulating the existing algorithms in MATLAB.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U463.6;TP391.41

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