不均勻照度環境的駕駛昏睡偵測與警示

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姓名

曹力仁(Li-Jen Tsao) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

不均勻照度環境的駕駛昏睡偵測與警示
(Driver Drowsiness Detection and Warning under Various Illumination Conditions)

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摘要(中)

昏睡與疲勞會降低對外界的反應、注意力，與警覺度。為了保障駕駛人和行人的安全，我們提出了一個駕駛昏睡偵測和警示系統；我們架設一部紅外線相機於駕駛座前方擷取駕駛人的臉部影像，分析駕駛人是否昏睡並提出警示。
此系統包含了主動式取像設備、眼睛偵測、眼睛追蹤、眼睛閉合與視線偵測，及昏睡判定與警示。為了使系統能也夠在夜晚或是亮度不足的環境下運作，我們使用一組紅外線相機配合紅外線打光器來取得駕駛人的臉部影像。
針對亮度不均勻的影像，我們根據影像的邊強度將影像劃分為多個區域，再對影像各區域各別二值化，經由幾何限制條件擷取可能的眼睛區塊，再利用支援向量機 (Support Vector Machine, SVM) 辨識是否為眼睛，最後再進行雙眼的驗證。根據雙眼的位置估計臉部範圍。當連續三張影像偵測成功，進入追蹤模式。在追蹤模式，我們在預測的區域內做三階段的眼睛偵測。在眼睛閉合偵測部份，我們測試了兩種方法並比較其準確性。在視線方向偵測，我們根據瞳孔的位置來判斷視線的左右方向。在昏睡判定方面，我們根據單位時間內閉眼張數所佔的百分比來判斷駕駛人是否陷入昏睡。
我們在不同照度環境下測試我們的系統。實驗數據提示，眼睛偵測的偵測率為88.6%，誤判率為1.16%；眼睛閉合偵測的正確率為93.5%，誤判率為6.39%；平均偵測時間為0.057045秒，每秒約可處理18張影像。我們的方法能夠正確的偵測到駕駛人的眼睛和閉合狀態，並在偵測到駕駛昏睡的0.9秒內發出警示

摘要(英)

To insure the safety of the driver and pedestrians on the road, we propose a vision based driver drowsiness detection and warning system. We use a camera mounted on the vehicle to capture the driver’s face images for eye detection and drowsiness discrimination.
The system consists of five parts: Image acquisition system, eye detection, eye tracking, eye open/close and gaze direction estimation, and drowsiness discrimination. In the image acquisition system, we use an IR camera with two illuminators to capture driver’s face images in poor illuminated conditions.
In order to deal with the uneven illumination, we propose a local thresholding method to divide the image into several partitions based on the strong edges then iteratively threshold each partition. We use connected-component and support vector machine (SVM) to verify eyes. If there are fixed numbers of frames succeeded in detection mode, we alternate the processing to tracking mode. In tracking mode, we detect eyes in the predicted region. We extract eye open/closed statuses and gaze directions information as our visual cues. In eye open/closed statues determination, we consider two criteria and compare their performance. In gaze direction estimation, we divide the eye region into three equal-sized subregions, then determine the pupil location in which subregion for the estimation. In drowsiness discrimination, we use PERCLOS measurement to judge whether the driver is drowsy.
We test our system on our experimental car in various illumination conditions such as sunny day, cloudy day, at night, uneven illuminated conditions, with/without glasses. From the experimental results, we find that the proposed approach can stably detect the eyes and give a warning if drowsiness is detected.

關鍵字(中)

★ 眼睛偵測
★ 支援向量機
★ 眼睛閉合偵測
★ 昏睡判定

關鍵字(英)

★ eye detection
★ support vector machine
★ eye open/closed detection
★ drowsiness discrimination

論文目次

摘要 II
誌謝 IV
目錄 V
第一章緒論一
第二章相關研究二
第三章眼睛偵測三
第四章眼睛追蹤四
第五章視覺線索的擷取與昏睡判定五
第六章實驗七
第七章結論八
附錄英文版論文九
Abstract ii
Contents iv
List of Figures vi
List of Tables x
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 System overview 2
1.3 Thesis organization 3
Chapter 2 Related Works 5
2.1 Techniques for detecting driver drowsiness 5
2.2 Eye detection 9
2.3 Face detection 12
Chapter 3 Eye Detection and Verification 15
3.1 Dividing the image into several regions 17
3.1.1 Divide the image vertically 18
3.1.2 Divide the image horizontally 22
3.2 Iterative thresholding in each region 24
3.3 Connected-component generation 25
3.4 Geometric constrains 26
3.5 Eye verification using support vector machine (SVM) 27
3.5.1 An introduction to support vector machine 28
3.5.2 Training data for SVM 30
3.6 Verification of an eye-pair 32
Chapter 4 Eye Tracking 33
4.1 Predict the eye position 35
4.2 Three strategies for eye verification 37
4.3 Face position estimation 38
Chapter 5 Visual Cues Extraction and Drowsiness Discrimination 43
5.1 Eye open/closed status determination 43
5.1.1 Eye open/closed status classifying by SVM 44
5.1.2 Eye open/closed judgment by the ratio of eye’s height 45
5.2 Drowsiness discrimination 47
5.3 Gaze direction estimation 47
Chapter 6 Experiments 50
6.1 The development environment 50
6.2 Experimental results 51
6.2.1 Eye detection and tracking 53
6.2.2 Eye status estimation 59
6.2.3 Drowsiness discrimination by PERCLOS measurement 60
6.2.4 Local maxima searching of the projection chart 63
6.2.5 Local thresholding 64
6.2.6. Eye open/close determination 66
Chapter 7 Conclusions and Future Works 68
7.1 Conclusions 68
7.2 Future works 68
References 70

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指導教授

曾定章(Din-Chang Tseng)

審核日期

2008-7-16

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