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論文名稱 Title |
以模糊推論為基礎之夜間行人辨識系統 Night Pedestrian Detection System Based On Fuzzy Reasoning |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
69 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2012-07-25 |
繳交日期 Date of Submission |
2012-08-16 |
關鍵字 Keywords |
影像處理技術、夜間行人辨識、近紅外線攝影機、模糊推論系統、行人頭部與軀幹 Image Processing Technology, Near-Infrared Camera, Night Pedestrian Detection, The Pedestrian Upper Region Feature, Fuzzy Reasoning system |
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統計 Statistics |
本論文已被瀏覽 5662 次,被下載 322 次 The thesis/dissertation has been browsed 5662 times, has been downloaded 322 times. |
中文摘要 |
夜間行人辨識系統之研究已發展多年,近年來也被廣泛的運用於機械視覺中,例如:防盜監視系統,智慧型車輛夜視輔助系統等,在這些系統中,運用了許多行人辨識之複雜演算法,本文希望能夠提供一套不用過於複雜的演算法,也能有不錯辨識率的夜間行人辨識系統。本論文以近紅外線攝影機做為影像來源,將行人設定在10-15m的範圍內,因在夜間視線不佳的關係許多特徵皆不明顯,故選擇行人上半身做為可利用之特徵,利用影像處理技術將行人頭部與軀幹之特徵參數一一計算出,本文又將行人分成單一行人與重疊行人,提出上半部頂點大於二就有能為兩個以上的行人重疊。最後導入了模糊推論,模糊推論系統的優點是不需要精確的物理模型且可將人類的專業經驗應用在系統當中。故本文將影像處理後得到之行人頭部與軀幹之特徵參數以模糊推論的方式判斷影像中是否有夜間行人之存在,以達成不需過多數學理論基礎之下辨識出夜間行人之目的。 |
Abstract |
none |
目次 Table of Contents |
目錄 論文審定書 i 誌謝 ii 中文摘要 iii 圖索引 vi 表索引 viii 第一章 緒論 1 1-1研究背景 1 1-2文獻回顧 3 1-3研究目的 4 1-4研究步驟圖 5 1-5本文架構與流程 6 第二章 影像處理技術介紹 7 2-1影像前處理流程圖 7 2-2影像前處理 8 2-2-1灰階化 8 2-2-2中值平滑濾波器 9 2-2-3型態學運算 10 2-2-4 ROI選取 12 2-2-5細線化 15 2-2-6頂點與交叉點 17 2-3 行人特徵擷取 19 2-3-1上半部頂點與交叉點選取 19 2-3-2頭部與軀幹夾角 21 2-3-3頭部與軀幹寬度 21 第三章 模糊理論及控制 23 3-1模糊集合 23 3-2模糊關係 26 3-3模糊推論 27 3-4模糊化及解模糊化 29 3-4-1模糊化 30 3-4-2解模糊化 32 3-5模糊控制 33 第四章 模糊推論行人 35 4-1夜間行人觀察 36 4-2歸屬函數 38 4-3模糊規則 41 第五章 實驗結果與效能評估 42 5-1系統硬體 42 5-1-1筆記型電腦 43 5-1-2紅外線攝影機與紅外線投射器 43 5-1-3影像擷取器 46 5-2系統軟體 46 5-3實驗結果 47 5-4效能評估 48 第六張 結果討論與未來方向 55 6-1結果討論 55 6-2未來方向 55 參考文獻 57 |
參考文獻 References |
[1]行政院衛生署網站-歷年死因統計 http://www.doh.gov.tw/CHT2006/index_populace.aspx [2]交通部,交通白皮書 [3]http://news.u-car.com.tw/13411.html [4]A. Broggi, M. Bertozzi, A. Fascioli, and M. Sechi, Shape-Based ”Pedestrian Detection ,” IEEE Intelligent Vehicles Symposium, pp. 215-219, 2000. [5] G. Ma, D. Muller, S.-B. Park, S. Muller-Schneiders, and A. Kummert, “Pedestrian Detection Using a Single-monochrome Camera ,”IET Intelligent Transport Systems, Vol. 3, No. 1, pp. 42-56, 2009. [6]Shashua,A “Pedestrian detection for driving assistance systems: single-frame classification and system level performance,” Intelligent Vehicles Symposium, 2004 IEEE, pp.1-6,14-17 June 2004 [7] F. Suard, A. Rakotomamonjy, A. Bensrhair and A. Broggi, Pedestrian, “Detection using Infrared image and Histogram of Oriented Gradients,” Intelligent Vehicles Symposium, pp. 206-212, 2006. [8]N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886-893, 2005. [9]M. Bertozzi, A. Broggi, M. Del Rose, M. Felisa, A. Rakotomamonjy and F. Suard, “APedestrian Detector Using Histograms of Oriented Gradients and a Support VectorMachine Classifier, ” IEEE Transactions on Intelligent Transportation Systems, pp. 143-148, 2007. [10] J. Ge, Y. Luo and G. Tei, “Real-Time Pedestrian Detection and tracking at Nighttime for Driver-Assistance Systems,” IEEE Trans. Intelligent Transportation Systems, vol. 10, no. 2, June. 2009. [11] D. M. Gavrila, “Pedestrian Detection from a Moving Vehicle, ” Proceedings of the European Conference on Computer Vision (ECCV), 2000 [12] Haritaoglu I., Harwood D. and Davis L. S.,“W4:Real-TimeSurveillance of People and Their Activities,” IEEE Transactions onPattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp.809-830, Aug, 2000. [13] Fujiyoshi and A. J. Lipton, “Real-Time Human Motion Analysis by Image Skeletonization,” IEEE workshop on Application of Computer Vision, pp.15 - 21, 1998. [14] A. Broggi, R. I. Fedriga, A. Tagliati, T. Graf, and M. Meinecke, "Pedestrian Detection on a Moving Vehicle: an Investigation about Near Infra-Red Images," Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 431-436, 2006 [15] Y. Ran, Q. Zheng, I. Weiss, L. S. Davis, W. A. Almageed, and L. Zhao, “Pedestrian Classification From Moving Platforms Using Cyclic Motion Pattern, “ IEEE International Conference on Image Processing, vol. 2, pp. 854-857, 2005 [16] Machine Vision, First Edition (1995), by Ramesh Jain, Rangachar Kasturi and Brian G. Schunck, ISBN: 0-07-032018-7, McGraw-Hill, Inc. [17] L.A. Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8, pp. 338-353, 1965. [18] E. H. Mamdani and S. Assilian, “An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller,” International Journal of Man-Machine Studies, vol. 7, pp. 1-13, 1975. [19] A. Broggi, A. Fedriga, A. Tagliati, and M. Meinecke, “Pedestrian Detection on a Moving Vehicle: an Investigation about Near Infra-Red Images,” IEEE Intelligent Vehicles Symposium, pp. 431-436, 2006. [20] C. C. Chen, J. W. Hsieh, Y. T. Hsu, and C. Y. Huang, “Segmentationof Human Body Parts Using Deformable Triangulation,”International Conference on Pattern Recognition, vol. 1, pp.355-358,2006. |
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