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研究生: 蔡瑞哲
Tsai, Jui-Che
論文名稱: 具頭動補償之高速可見光眼動儀系統平行架構設計
Parallel Computing Architecture of High Speed Visible Light Gaze Tracking System with Head Motion Compensation
指導教授: 高文忠
Kao, Wen-Chung
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2017
畢業學年度: 105
語文別: 中文
論文頁數: 74
中文關鍵詞: 眼動儀可見光平行化計算頭動補償
英文關鍵詞: parallel architecture, multithreading, head motion compensation
DOI URL: https://doi.org/10.6345/NTNU202202830
論文種類: 學術論文
相關次數: 點閱:114下載:3
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  • 眼動儀可應用於學習與認知心理學、商業廣告行為、神經科學等領域,利用眼睛觀看位置的資料進行統計分析,研究人類觀看行為的差異。目前市售的眼動儀多數使用紅外光技術,缺點為環境中的紅外光源會影響系統準確率,因此許多企業及學術單位投入於可見光眼動儀開發,但至今市面上尚未出現高精確度的可見光眼動儀產品。

    本文以既有的眼球模型,改良虹膜抓取方法,針對一般辦公室光源環境下,使用每秒輸入480張影像之高速相機記錄眼睛影像。另一方面,利用高速相機影像連貫性的特徵,抓取眼睛固定特徵進行頭動補償,搭配平行化架構設計,以多執行序的技術,使本眼動儀計算速度可達到每秒480張影像,並達到高精確度與精準度的系統目標。

    Nowadays, the eye tracking system has been applied in the studies of learning and cognitive psychology, advertising design, neurosciences, and other fields. That is, the statistical analysis of the gaze data has made it possible to study the human behavior. Currently, most of gaze tracking systems are designed by equipped with an infra-ray (IR) light source, but the accuracy of an IR-based gaze tracking system can be affected by the illumination in the environment. Therefore, more and more researchers devoted themselves to the development of visible light eye tracking system. Still, few systems have been designed successfully which could reach a satisfactory level of accuracy and speed.
    The proposed system aims to improve the system accuracy as well as speed by modifying the iris matching algorithm. The breakthrough comes from the modifications of image preprocessing, head movement compensation, optimal iris matching, and a novel software architecture design based on parallel computing scheme. The experimental result shows that the proposed system has reached a process speed of 480 frames/s with a promising accuracy as well as precision result.

    摘要 i ABSTRACT ii 誌謝 iii 目錄 i 圖目錄 iv 第一章 緒論 1 1.1 眼動追蹤系統應用 1 1.2 眼動追蹤技術發展 2 1.3 眼動追蹤技術目標 3 1.4 論文架構 5 第二章 文獻探討 6 2.1 虹膜中心辨認方法 6 2.1.1 文獻研究 6 2.1.2 選擇方法 11 2.2 頭動補償 11 2.2.1 頭動補償的必要性 11 2.2.2 頭動補償方法 13 第三章 軟體系統架構 16 3.1 系統流程圖 16 3.2 建立眼球模型 17 3.3 第一張影像眼角偵測 18 3.4 建立映射模型 23 3.4.1 接續影像眼角偵測 23 3.4.2 三階段虹膜圓偵測 26 3.5 頭動補償 30 3.6 計算映射模型參數 32 3.7 眼睛凝視點位置計算 34 3.8 平行化系統架構預測加速 34 3.8.1 平行化架構 34 3.8.2 預測加速 38 第四章 研究成果 41 4.1 實驗環境 41 4.2 三階段虹膜圓偵測 44 4.2.1 ∆α大小對搜尋結果的影響 44 4.2.2 三階段的參數對結果的影響 45 4.2.3 三階段虹膜圓偵測分別速度 47 4.3 平行化架構 47 4.3.1 多執行緒數量比較 47 4.3.2 多執行緒中連續影像數量比較 49 4.3.3 預測多執行緒中連續影像數量比較 50 4.4 眼角偵測 53 4.4.1 第一張眼角搜尋範圍 53 4.4.2 後續影像眼角偵測範圍 53 4.4.3 絕對誤差和算法(SAD)第二階段眼角偵測 57 4.4.4 絕對誤差和算法(SAD)比對對象 58 4.4.5 眼角偵測結果及速度 59 4.5 頭動補償 61 4.5.1 頭部不動進行頭動補償 62 4.5.2 頭部大範圍移動進行頭動補償 63 4.6 整體系統 66 4.6.1 系統精準度 66 4.6.2 系統速度 68 4.7 系統限制 68 第五章 結論及未來展望 69 5.1 結論 69 5.2 未來展望 69 參考文獻 71 自傳 73 學術成就 74

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    [23] GT72 6QE DOMINATOR PRO G TOBII https://www.msi.com/Laptop/GT72-6QE-DOMINATOR-PRO-G-tobii.html#hero-overview
    [24] 眼動與閱讀實驗室http://emrlab.nccu.edu.tw/em_q&a.html#qa9_top

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