本論文設計的兩大系統對於智慧型交通監控系統中，其最主要貢獻有兩項，分別達成自適性夜晚車輛偵測與追蹤，以及駕駛疲勞與分心狀態偵測之目的，分別簡述於下。
自適性夜晚車輛偵測與追蹤可分為兩部分做探討，分別為追蹤系統與車輛偵測。對於追蹤系統方面，將狀態分為Appear、Tracked、Leave和Disappear四種，並搭配與前一刻的交集圖的使用，能迅速有效的在追蹤區域內，順利的追蹤與更新每部車輛的狀態；車輛偵測方面，設計車輛驗證機制在車輛進入追蹤區時判定是否為車輛，並於追蹤區域內排除誤判之車輛；由於實際道路監視器對於聚焦遠近與離地面距離皆不一的情況下，為能符合適用性，因此設計自適應的遮罩得在各不同環境能更彈性的應用與實現。實驗結果顯示，本論文提出的方法能達到即時偵測與計數，並且在應用於不同車道環境下也能保持程度以上的精準度。
駕駛疲勞與分心偵測系統中，本論文針對三樣即時事件做偵測，分別為分心、不正常眨眼和瞌睡，且能同時評估駕駛目前疲勞程度；偵測進行中同步計算眨眼率以及閉眼百分比 (PERCLOS)，利用離線訓練之迴歸分析參數，轉換為駕駛疲勞程度；事前訓練之目的，是利用迴歸模型描述眨眼率、閉眼百分比與駕駛疲勞程度之關係。偵測方面利用OpenCV人臉以及眼睛偵測後，利用自帶訓練器偵測是否閉眼，有效率的計算閉眼百分比以及眨眼率，進而判斷不正常眨眼以及瞌睡事件；分心偵測又分為頭部轉向分心以及凝視旁側分心，頭部轉向是記錄正臉移動軌跡，臉轉向後可分析頭部最有可能之轉動方向；凝視方向為偵測瞳孔位置後，兩瞳孔連線之中心點與臉部中心點連線，和水平呈現的夾角做比較。於實驗結果顯示，本系統能有效的判斷駕駛分心、不正常眨眼以及打瞌睡的即時事件，也能利用所訓練之迴歸参數有效描述駕駛當下疲勞的程度。

This thesis presents two contributions 1) the night-time vehicle detection and tracking with an adaptive mask training, and driver fatigue and distraction detection based on eye-state analysis.
In night-time vehicle detection and tracking system, the vehicle tracking module is designed to classify four status: appearing, tracking, leaving, and disappearing. Tracking can be efficiently achieved in the tracking area by matching with the intersection map. In vehicle detection module, a vehicle verification mechanism is designed to exclude false detected vehicles when passing through the tracking area. Considering that in practical scenario each surveillance camera on the road may capture vehicles of different distances and sizes, an adaptive mask is trained to tackle with the problem. The result shows that the system can achieve real-time processing detection and tracking.
In the second half of this thesis, three events (driver distraction, abnormal eye blinking, and drowsiness) and driver fatigue level are evaluated with the proposed driver fatigue and distraction system. The eye blinking rate and percent of closure (PERCLOS) are employed during detection, as important clues for determination of driver abnormal eye blinking, and drowsiness events, the processed data is also converted to driver fatigue level by the off-line regression trained parameters, which is for fitting the relation between blinking rate and PERCLOS and driver fatigue level. Face and opened eyes are detected by cascade classifers trained from AdaBoost. Moreover, a self-trained closed eye classifer is utilized for closed eyes. Driver distraction includes head turning and eye gazing on either left or right side. A driver’s head is regards as turned when frontal face is no longer detected. In this case, the trace of head motion is estimated for the determination of the direction of head turning. Distracted Eyes gazing is detected by computing angles between the center of two irises and the vertical face center. The experimental results show that the system is able to determine the distraction, abnormal eye blink, and drowsiness events of a driver, and is capable of evaluating the driver fatigue level of a driver.

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