使用特徵變化與瞳孔偵測來分析危安駕駛

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洪紹翔(Shao-Shan Horne) 查詢紙本館藏

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資訊工程學系

論文名稱

使用特徵變化與瞳孔偵測來分析危安駕駛
(Hazardous Driver Behavior Analysis Using Pupil Detection and Feature Variation)

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摘要(中)

在現代化社會中，由於車輛交通意外發生頻繁，使得社會成本增加頗遽。台灣年平均事故已達六萬多件，依據統計，其中九成以上肇事因素皆人為所致。在本論文中，我們設計一個以安全監控為基礎之系統，對車內駕駛進行臉部偵測、追蹤與危險狀態分析，以便提前發現徵兆，並警示不專心、疲勞之駕駛注意突發狀況，預防意外發生。
系統主要由下列四個部分組成。第一部份為“臉部偵測”。使用改良式YCbCr色彩模組尋找膚色區域，結合平滑濾波邊緣偵測法修補缺失特徵，更用對數相減法補償區域性亮度不均問題，使臉部搜尋具有更高穩定性，比已知的膚色模組精確許多。
第二部份為“臉部追蹤”。計算相鄰畫面臉部間之關連性，校正候選目標之新邊界，並增加系統之精確性。臉部追蹤可大幅度降低時間複雜度。系統亦修正常見之部分遮蔽及後座人臉所形成的追蹤錯誤。
第三部分為“特徵分析與標記”，除了進一步提升臉部搜尋精確性，系統亦可找出眼(耳)、鼻確切位置。此外，使用有名的三角演算法計算角度，以判定臉部方向。對於不同大小之人臉、相異的姿勢與表情、亮度不均或雜訊，皆能正確處理。
第四部分為“危險分析”。藉由交通部之意外統計資料，定義五類危安事件相異權值，包含自動分析轉頭聊天、疲勞、使用手機、連續低頭及全臉遮蔽，藉由方向估計、瞳孔偵測、特徵變化及手部膚色搜尋等。
實驗由大量測試媒體影像序列得來，精確率平均達91％。且系統之FRR(False Rejection Rate)與FAR(False Acceptance Rate)皆低於10%，證明處理相異之危安事件時，我們提出之方法方法穩定且可行。

摘要(英)

Traffic accidents occur frequently in modernized society so that the society has to pay a lot of cost. There are more than 60,000 vehicle accidents occurring each year in Taiwan. Among them, over ninety percent of accidents are caused due to the careless of drivers according to the statistical analysis.In this thesis, a surveillance-based system utilizing face detection, tracking, and hazardous status monitoring of drivers is designed. By monitoring the symptoms of non-concentration or fatigue of drivers, a warning signal can be issued in advance so as to preventing the occurring of accidents due to the lack of unawareness.
The proposed system is composed of four main parts. The first part is face detection. The modified version of YCbCr color space is adopted to obtain raw skin color images. Edge smoothing operation is employed to remedy the erroneous judgments of extracted features. Moreover, logarithmic intensity difference method is devised to compensate partial illumination. These algorithms make the task of face detection more robust and have higher accuracy than known skin-color model.
The second part is face tracking. Correlation operation is manipulated on current face and records the ones which can regulate the borders and increase the accuracy. Time complexity of the proposed method can be drastically decreased due to the performing of face tracking.
The third part is feature inspection and marking. Our proposed method can not only promote the accuracy but also mark the exact positions of features. A novel triangular-based theorem is adopted to calculate the angles of features to determine whether the considered face is frontal or profile. Moreover, it can conquer the problems of different face sizes, varying lighting conditions, varying expressions, and noises.
The forth part is the analysis of dangerous behaviors. According to statistical results conducted by the Ministry of Communications, different weighs are assigned for five hazardous events which may result in accidents. The proposed system can automatically analyze the hazardous behaviors of chatting, drowsing, phone using, consecutive head lowering, and facial occlusion by performing direction estimation, pupil detection, feature variation, etc.
Experiments were conducted on a variety of testing video sequences. An approximately 91% success rate can be achieved; besides with both false rejection rate and false acceptance rate being very low (near 10%). Experimental results reveal the feasibility and validity of our proposed system in monitoring various hazardous behaviors resulting from drivers.

關鍵字(中)

★ 疲勞分析
★ 人臉偵測
★ 影像監控

關鍵字(英)

★ fatigue analysis
★ video surveillance
★ face detection

論文目次

英文摘要 ……………………………Ⅰ
中文摘要 ……………………………Ⅲ
總目錄 ……………………………Ⅴ
附圖目錄 ……………………………Ⅶ
表格目錄 ……………………………Ⅸ
第一章序論…………………………1
1.1 研究動機…………………………1
1.2 文獻回顧…………………………2
1.2.1 駕駛輔助系統……………2
1.2.2 臉部偵測與追蹤…………3
1.2.3 方向計算與亮度修正……4
1.3 論文架構…………………………5
1.3.1 膚色影像…………………5
1.3.2 偵測追蹤與特徵搜尋……6
1.3.3 權值定義及危安分析……6
第二章駕駛安全輔助系統…………7
2.1 視訊監控設計……………………7
2.2 系統流程…………………………9
2.3 技術問題與克服方式……………12
第三章人臉偵測與追蹤……………14
3.1 人臉偵測…………………………14
3.1.1 原始膚色模組……………14
3.1.2 平滑邊緣運算……………16
3.1.3 臉部特徵連接……………18
3.1.4 以對數相減作光度修正…20
3.1.5 八方向膚色區塊連結……25
3.2 人臉追蹤…………………………26
3.2.1 相鄰畫面重疊臉匹配……27
3.2.2 相同畫面候選臉合併……27
3.2.3 臉界偵測與計算…………28
3.2.4 部分遮蔽之修正…………30
3.3 特徵檢驗與標記…………………32
3.3.1 眼(耳)、鼻區域搜尋……32
3.3.2 特徵位置計算……………34
3.3.3 平均值與變異數…………35
3.3.4 特徵距離驗證……………36
3.4 人臉方向計算……………………37
3.4.1 三角計算法………………37
3.4.1 改良式三角計算法………38
第四章駕駛狀態分析………………42
4.1 危險權值定義……………………42
4.1.1 危安事件統計……………42
4.1.2 權值定義與警示條件……44
4.2 方向判定與轉頭聊天分析………46
4.3 瞳孔偵測與疲勞分析……………49
4.4 不當手機使用分析………………54
4.5 遮蔽及過低人臉分析……………55
第五章實驗結果……………………58
5.1 偵測追蹤精確率 …………………58
5.2 危安判定精確率 …………………60
第六章結論與未來工作……………61
6.1 結論………………………………61
6.2 未來工作…………………………62
參考文獻………………………………64

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指導教授

范國清(Kuo-Chin Fan)

審核日期

2005-7-8

推文