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研究生: 許雅淳
Ya-Chun Hsu
論文名稱: 使用單一網路攝影機之視線判斷
Gaze Estimation Using Single Webcam
指導教授: 李忠謀
Lee, Chung-Mou
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2013
畢業學年度: 101
語文別: 中文
論文頁數: 51
中文關鍵詞: 人臉偵測人眼偵測虹膜偵測視線判斷
英文關鍵詞: Face detection, Eye detection, Iris detection, Gaze Estimation
論文種類: 學術論文
相關次數: 點閱:108下載:5
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  • 眼動追蹤一直被應用於認知心理學相關的研究,近年來眼動追蹤更成為人機互動相當熱門的發展重點之一。事實上,眼動追蹤不但能夠被用於輔助行動不便的病患透過電腦與人溝通,也能應用於偵測駕駛精神狀態上,減少駕駛因過度疲勞造成的車禍率,除了可挽救許多人命外,更可降低社會成本。
    然而,市面上的眼動追蹤系統經常價格不斐且不易取得,因此我們提出一個只要個人電腦及一個網路攝影機就能使用的眼動追蹤方法。我們修改了Adaboost的人臉追蹤方式,以期調高偵測速度並降低偵測錯誤率,也提出一個能夠快速尋找到虹膜中心位置的方法。最後透過支持向量機,判斷視線可能坐落的區塊,再透過我們設計的視線追蹤機制,進行最終視線所在區塊的判斷。

    Eye-tracking systems are wildly used in cognitive psychology research problems, and recently eye-tracking technology has been considered as a potential multimedia interaction way. It can be applied to help people who suffer from disease and lost the ability of controlling their movements, so that they can manipulate computers and communicate with others. In addition, eye-tracking system can also be used to detect drivers’ fatigue. Reducing the number of accidents can not only save lives but also decrease society cost.
    However, commercially available eye-tracking systems usually endure with high cost and hard to fetch problems. We propose an eye movement tracking method using personal computer and single webcam. Our method modifies the Adaboost face detection algorithm to make it faster and reduce the false positive rate. We also provide a new method to calculate the center of iris quickly. Finally, we use SVM to help us categories possible gaze region and determine the final gaze region with our gaze tracking mechanism.

    摘要 I ABSTRACT II 目錄 III 圖目錄 V 第一章 緒論 1 1.1研究背景 1 1.2 研究目的 2 1.3 研究範圍與限制 3 1.4 論文架構 3 第二章 文獻探討 4 2.1人臉之偵測方式 4 2.1.1 基於特徵的方式(Feature-Based) 4 2.1.2 樣板匹配的方式(Template Matching) 5 2.1.3 基於外觀的方法(Appearance-Based) 5 2.2 人眼之偵測方式 6 2.2.1 基於形狀的方式(Shape-based approach) 6 2.2.2 基於外觀的方法(Appearance-based approach) 7 2.2.3 基於學習的方法(Learning-based approach) 7 2.3瞳孔之偵測方式 8 2.3.1樣板匹配的方式(Template Matching) 8 2.3.2 基於特徵的方式(Feature-based approach) 8 2.4 視線所在之判定方式 9 2.4.1基於外觀的方法(Appearance-Based) 9 2.4.2 類神經網路(Neural Network) 9 第三章 研究方法 10 3.1 研究目標 10 3.2 系統架構與流程 11 3.3 人臉偵測 12 3.3.1 灰階影像(Graylevel Image)轉換 13 3.3.2 膚色偵測(Skin Color Detection) 13 3.3.3 伽馬校正(Gamma correction) 14 3.3.4 積分影像(Integral Image) 15 3.3.5 矩形特徵 16 3.3.5 階層分類器(Cascade of Classifier) 17 3.3.6 人臉區塊之判定 18 3.4 人眼偵測 19 3.5瞳孔偵測 20 3.5.1 OTSU演算法二值化門檻值決定 21 3.5.2 Canny邊緣偵測 23 3.5.3 虹膜中心(瞳孔)偵測 24 3.5.4 虹膜中心可信度 31 3.5.5 上眼瞼 32 3.6 眼睛注視方向判定 33 3.6.1 支持向量機(Support Vector Machine, SVM) 34 3.6.2 視線區塊之追蹤 35 第四章 實驗結果與分析 36 4.1 實驗方法與評估方式 36 4.2實驗結果 38 4.2.1 人臉偵測 38 4.2.2 虹膜偵測 42 4.2.3 注視區域判斷 44 第五章 結論 47 5.1 結論 47 5.2 未來研究 47 參考文獻 49

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