以核方法化的相關濾波器之物件追蹤方法 實作眼動儀系統

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姓名

鄭博仁(Bo-Ren Zheng) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

以核方法化的相關濾波器之物件追蹤方法實作眼動儀系統
(Implementation of an eye-tracking system using Kernelized Correlation Filter.)

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摘要(中)

近年來，眼動儀已經是一個常用於心理學分析、疾病分析、廣告配置分析等等領域的一套設備。在本研究中，我們自製了一套穿戴式的眼動儀系統。使用Microsoft HD-6000 相機，改造成可清楚拍攝虹膜與瞳孔邊界的紅外光相機，搭配專用連接器可將攝影機置於眼鏡的鏡框上，讓攝影機能夠拍攝到眼睛的紅外光影像。眼動儀系統最主要的功能是能夠正確的偵測瞳孔的位置，本研究方法使用了以核方法的相關濾波器之物件追蹤演算法實作瞳孔追蹤功能，使用追蹤演算法找出大致上的瞳孔中心位置，並且搭配圓形擬合方法找出正確的瞳孔圓心及半徑，在搭配投影轉換，將瞳孔的位置轉換到實際所看的螢幕位置。在實驗結果中，與手動定位的瞳孔圓心的誤差平均只有2.02 個pixel，半徑大小的誤差為1 個pixel，且在執行速度上，處理一張影像只需要0.0295 秒，相當於每秒可執行33.95 張影像，執行速度超過一般攝影機所能提供的每秒30 張影像，是一套計算快速且準確的眼動儀系統。

摘要(英)

In recent years, eye-tracking is already used in areas like psychology, human-computer interface and e-learning. In this study we made a wearable eye-tracking system.We hand-made a IR camera by modifying a commercially available webcam(MS HD-6000) and mounted it to a customized glasses frame. Such device is a wearable eye-tracking system which is able to record a clear video of eye movement when the user wears the glasses frame. The most important feature of an effective eye-tracker is to locate and track the pupil movement correctly in realtime. In this research, we used Kernelized Correlation Filter to implement pupil location tracking. By using KCF and a self-developed circle-fitting algorithm, we are able to detect and track the pupil location accurately. In experimental results,compare manual and automatic detection of the pupil center, the average error of manual and automatic detection pupil center is 2.02 pixel, and the average error ofpupil radius is 1.1 pixel. In terms of execution speed, it only takes 0.0295 second to process an image, which is equivalent to 33.9 FPS (frame per second). Therefore,our eye-tracking system is fast enough to fulfill the real-time requirement and is ready to be used in many practical situations.

關鍵字(中)

★ 眼動儀
★ 相關濾波器
★ 核方法

關鍵字(英)

★ eye-tracking
★ correlation filter
★ Kernel method

論文目次

中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
一、緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1-1 研究背景. . . . . . . . . . . . . . . . . . . . . . . . . 1
1-2 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . 3
1-3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . 4
二、眼動儀介紹. . . . . . . . . . . . . . . . . . . . . . . . 5
2-1 眼動儀簡介與演算法. . . . . . . . . . . . . . . . . . . 5
2-2 眼動儀系統比較. . . . . . . . . . . . . . . . . . . . . 6
2-3 硬體介紹. . . . . . . . . . . . . . . . . . . . . . . . . 8
2-3-1 移除紅外光濾鏡. . . . . . . . . . . . . . . . . . . . . 9
2-3-2 紅外光源建置. . . . . . . . . . . . . . . . . . . . . . 10
三、眼動儀系統核心演算法. . . . . . . . . . . . . . . . . 13
3-1 演算法簡介. . . . . . . . . . . . . . . . . . . . . . . . 13
3-1-1 Kernel type . . . . . . . . . . . . . . . . . . . . . . . . 14
1. Gaussian Kernel . . . . . . . . . . . . . . . . . . . . . . 15
2. Polynomial Kernel . . . . . . . . . . . . . . . . . . . . 15
3-1-2 Feature type . . . . . . . . . . . . . . . . . . . . . . . . 16
1. 方向梯度直方圖. . . . . . . . . . . . . . . . . . . . . 16
3-2 Kernelized Correlation Filters . . . . . . . . . . . . . . 19
3-2-1 Linear regression . . . . . . . . . . . . . . . . . . . . . 19
3-2-2 Circulant Shifts and Circulant Matrix . . . . . . . . . . . 20
3-2-3 Kernel trick . . . . . . . . . . . . . . . . . . . . . . . . 22
3-2-4 Detection . . . . . . . . . . . . . . . . . . . . . . . . . 24
3-2-5 Kernel Correlation . . . . . . . . . . . . . . . . . . . . 25
3-3 偵測瞳孔大小. . . . . . . . . . . . . . . . . . . . . . 26
3-4 圓形擬合方法. . . . . . . . . . . . . . . . . . . . . . 27
3-5 程式流程. . . . . . . . . . . . . . . . . . . . . . . . . 28
四、系統介紹. . . . . . . . . . . . . . . . . . . . . . . . . 32
4-1 校正. . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4-2 螢幕投影方法. . . . . . . . . . . . . . . . . . . . . . 32
4-2-1 Affine Transform . . . . . . . . . . . . . . . . . . . . . 33
4-2-2 二次線性回歸. . . . . . . . . . . . . . . . . . . . . . 35
4-3 系統實作與介面設計. . . . . . . . . . . . . . . . . . . 35
五、實驗結果. . . . . . . . . . . . . . . . . . . . . . . . . 40
5-1 Database . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5-2 瞳孔定位分析. . . . . . . . . . . . . . . . . . . . . . 41
5-2-1 Ground truth 誤差. . . . . . . . . . . . . . . . . . . . . 41
5-2-2 K-means Integration of Radial Difference(KIRD) . . . . 42
5-2-3 瞳孔定位實驗結果分析. . . . . . . . . . . . . . . . . 45
5-3 螢幕投影. . . . . . . . . . . . . . . . . . . . . . . . . 48
六、結論與未來展望. . . . . . . . . . . . . . . . . . . . . 51
6-1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
6-2 未來展望. . . . . . . . . . . . . . . . . . . . . . . . . 51
索引. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

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指導教授

栗永徽(Yung-Hui Li)

審核日期

2016-8-18

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