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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 葉丙成(Ping-Cheng Yeh) | |
| dc.contributor.author | Chun Lee | en |
| dc.contributor.author | 李俊 | zh_TW |
| dc.date.accessioned | 2021-06-16T03:57:11Z | - |
| dc.date.available | 2020-02-04 | |
| dc.date.copyright | 2015-02-04 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-12-04 | |
| dc.identifier.citation | [1] Huey, Edmund, ”The Psychology and Pedagogy of Reading (Reprint)”, MIT Press
1968 (originally published 1908). [2] Buswell G.T., ”Fundamental reading habits”, University of Chicago Press 1922. [3] Ba Linh NGUYEN, ”Eye Gaze Tracking”, Computing and Communication Technologies, 2009. RIVF ’09. International Conference on [4] P. Viola, M. Jones, ”Rapid Object Detection using a Boosted Cascade of Simple Feature”, IEEE vol. 2, 2001. [5] Shigang Chen, Xiaohu Ma, Shukui Zhang, ”AdaBoost Face Detection Based on Haarlike Intensity Features and Multithreshold Features”, 2011 International Conference on Multimedia and Signal Processing. [6] R. E. Schapire and Y. Singer, ”Improved boosting algorithms using confidence-rated predictions”, Machine Learning, vol. 37, 1999, pp. 297–336. [7] Y. Freund and R. E. Schapire, ”Experiments with a new boosting algorithm”, Proc. Machine Learning: of the Thirteenth International Conference, 1996, pp. 148-156. [8] R. Lienhart, A. Kuranov, and V. Pisarevsky, ”Empirical analysis of detection cascades of boosted classifiers for rapid object detection”, DAGM’03, Sep. 2003, pp. 297-304. [9] S. Z. Li and Z. Q. Zhang, ”Floatboost learning and statistical face detection”, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 26, July 2004, pp. 1112–1123. [10] R. Lienhart, J. Maydt, ”An Extended Set of Haar-like Features for Rapid Object Detection”, vol. 1,no. 1, pp. 900-903, 2002. [11] Jean-Yves Bouguet, ”Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm”, Intel Corporation, Microprocessor Research Labs, OpenCV Document, 1999. [12] Jianbo Shi and Carlo Tomasi, ”Good features to track”, Proc. IEEE Comput. Soc. Conf. Comput. Vision and Pattern Recogn.,pages 593-600, 1994. [13] B. D. Lucas and T. Kanade, ”An iterative image registration technique with an application to stereo vision”, Proceedings of Imaging Understanding Workshop, pages 121-130, 1981. [14] F. Abdat, C. Maaoui and A. Pruski, ”Real Time Facial Feature Points Tracking with Pyramidal Lucas-Kanade Algorithm”, Human-Robot Interaction, Daisuke Chugo (Ed.), ISBN: 978-953-307-051-3, InTech, 2010. [15] K. Ki-Sang, J. Dae-Sik, C. H.-I., ”Real time face tracking with pyramidal lucaskanade feature tracker”, Computational science and its applications ICCSA 2007 4705: 1074–1082, 2007 [16] R.Belaroussi, Milgram, M., ”Face tracking and facial features detection with a webcam”, CVMP 2006, 2006. [17] Abdat, F., Maaoui, C., Pruski, A., ”Real facial feature points tracking with pyramidal lucas-kanade algorithm”, IEEE RO-MAN08, The 17th International Symposium on Robot and Human Interactive Communication, Germany, 2008 [18] David Schreiber, ”Generalizing the Lucas–Kanade algorithm for histogram-based tracking”, Pattern Recognition Letters archive Volume 29 Issue 7, May, 2008, Pages 852-861. [19] Schreiber, D., ”Robust template tracking with drift correction. Pattern Recognition”, Lett. 28 (12), 1483–1491, 2007. [20] Adam, A., Rivlin, E., Shimshoni, I., ”Robust fragments-based tracking using the integral histogram”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2006. [21] Baker, S., Matthews, I., ”Lucas–Kanade 20 years on: A unifying framework”, Internat. J. Comput. Vision 56 (3), 221–255, 2004. [22] R.Belaroussi, Milgram, M., ”Face tracking and facial features detection with a webcam”, CVMP, 2006. [23] J. Wu, S. C. Brubaker, M. D. Mullin and J. M. Rehg, ”Fast Asymmetric Learning for Cascade Face Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 30, Mar. 2008, pp. 369-382. [24] H. Yang and Y. Liu, ”A novel face detection method based on contourlet features”, Proc. International Conference on Intelligent Computing (ICIC), 2009, LNCS 5754, pp. 236-244. [25] Bradski, G., ”Computer vision face tracking as a component of a perceptual user interface”, Workshop Applications Computer Vision, 214–219, 1998. [26] Beymer, D., McLauchlan, P.F., Coifman, B., Malik, J., ”A real-time computer vision system for measuring traffic parameters”, Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 1997. [27] Hager, G.D., Belhumeur, P.N., ”Efficient region tracking with parametric models of geometry and illumination”, IEEE Trans. Pattern Anal. Machine Intell. 20 (10), 1025–1039, 1998. [28] Hager, G., Dewan, M., Stewart, C., ”Multiple kernel tracking with SSD”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2004. [29] Yang, C., Duraiswami, R., Davis, L., ”Efficient mean-shift tracking via a new similarity measure”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2005. [30] Yokoyama, M., Poggio, T., ”A contour-based moving object detection and tracking”, Second Joint IEEE Internat. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. [31] Zhao, Q., Tao, H., ”Object tracking using color correlogram”, IEEE 2nd Joint Internat. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 263–270, 2005. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55335 | - |
| dc.description.abstract | 科技蓬勃發展,人機互動的方式逐漸捨棄按鍵式的操作,例如在幾年之間,觸控手機逐漸取代按鍵式的手機,由此可以得知,科技產品的操作越來越直觀。但如果是行動不方便的人對於這樣需要碰觸器材的操作方式仍有不便,利用眼動偵測去控制器材的方式就可以解決這樣的問題。本論文'基於哈爾特徵及路卡斯-卡納德演算法之雙鏡頭偵測系統設計'就是期望用簡單的器材(如網路攝影機),可以進行眼動偵測。
本論文的系統架構分成三個部分:第一部分是在攝影機畫面中辨識出人臉,使用的方法就是哈爾特徵演算法(Haar feature algorithm),其核心的概念是利用數個簡單的矩形特徵來辨識畫面中的人臉。第二部分是追蹤第一部分辨識出來的眼睛,使用的方法是金字塔型路卡斯-卡納德光流檢測法(Pyramidal Implementation of the Lucas Kanade Feature Tracker),因為人的眼睛中,瞳孔跟眼白的對比是一個滿明顯的特徵點,利用路卡斯-卡納德演算法追蹤瞳孔在攝影機畫面中移動的軌跡。最後第三部分就是利用眼睛的位移推算出使用者人臉因為觀看的位置不同而轉動的幅度,用這樣的參數去預測使用者看螢幕上的哪個位置。並對不同設置做準確率的實驗,根據實驗結果找出最佳的設置,分析實驗結果提出最佳配置的假說,並用不同的器材來證明這假說是成立的,即得出不同條件下硬體設備如何設置會讓偵測的準確率最高。本論文設計出一套只要使用簡單的設備就可以達到眼動偵測,並且其他人能以一套準則重現最佳配置。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2021-06-16T03:57:11Z (GMT). No. of bitstreams: 1 ntu-103-R01942042-1.pdf: 6658252 bytes, checksum: c9a856972a1b394f3a03c9a73bc96f2a (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 1緒論1
1.1 前言. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 文獻回顧. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 系統設計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.4 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 人臉辨識及追蹤4 2.1 哈爾特徵演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 積分圖像. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.2 矩形哈爾特徵. . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.3 分類方程式. . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.4 層疊分類器. . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.1.5 實作結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 金字塔型路卡斯-卡納德光流檢測法. . . . . . . . . . . . . . . . . . . 16 2.2.1 圖像金字塔表示式. . . . . . . . . . . . . . . . . . . . . . . . 17 2.2.2 金字塔式光流計算. . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.3 迭代光流計算. . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3 雙鏡頭眼動偵測系統25 3.1 系統裝置. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 雙鏡頭眼動偵測. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3 系統運作流程. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4 實驗與分析37 4.1 實驗設置. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.2 實驗的結果與討論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.3 最佳配置. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5 結論及未來展望52 5.1 未來展望. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Bibliography 54 | |
| dc.language.iso | zh-TW | |
| dc.subject | 眼動偵測 | zh_TW |
| dc.subject | 哈爾特徵 | zh_TW |
| dc.subject | 網路攝影機 | zh_TW |
| dc.subject | 路卡斯-卡納德 | zh_TW |
| dc.subject | haar feature | en |
| dc.subject | webcam | en |
| dc.subject | eye tracking | en |
| dc.subject | lucas-kanade | en |
| dc.title | 基於哈爾特徵及路卡斯-卡納德演算法之雙鏡頭眼動偵測系統
設計 | zh_TW |
| dc.title | Dual Camera Eye Tracking System Design Based on Haar feature and Lucas-Kanade Algorithm | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 丁建均(Jian-Jiun Ding),簡韶逸(Shao-Yi Chien) | |
| dc.subject.keyword | 眼動偵測,網路攝影機,哈爾特徵,路卡斯-卡納德, | zh_TW |
| dc.subject.keyword | eye tracking,webcam,haar feature,lucas-kanade, | en |
| dc.relation.page | 57 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-12-04 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位: | 電信工程學研究所 | |
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| ntu-103-1.pdf 未授權公開取用 | 6.5 MB | Adobe PDF |
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