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姓名 彭振軒(Chen-Hsuan Peng) 查詢紙本館藏 畢業系所 資訊工程學系 論文名稱 使用樣板比對做進出口行人數量統計
(A Passing People Counting System using Template Matching Technique)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) 隨著取像設備價格大幅的降低,以及電腦科學的進步,以視覺為基礎的智慧型監控系統成為近年來熱門的研究主題。利用電腦視覺的方法,在不需要人為的操作之下,讓監控系統能夠自動對攝影機所擷取的影像進行分析,以具有偵測、追蹤及辨識的功能。而其好處,包括節省人力資源、降低成本和提供多樣化的服務。
本篇論文提出一個進出口人數統計的系統,是利用樣版比對的方式,判斷進出的物體是否為行人。首先利用背景相減法來找出前景物,接下來偵測部分,利用特徵比對的方式,辨識進出的行人,過濾其他的物體。最後則是利用追蹤來判定行人進出的方向,並統計各個方向的行人總數。
實驗部分是採用數段不同的行人進出影像,實驗結果顯示論文所提出的方法能準確且有效率的偵測並追蹤行人,達到統計進出人數的目的。摘要(英) With the price declining of capturing devices and the advancement of computer technologies recently, the topics of vision-based intelligent surveillance system have become more and more popular. Using the technique of computer vision without the needing of manual intervention, surveillance system can automatically analyze images captured by video camera to embed the functionalities of detection, tracking and identification. The advantages include saving human resources, saving cost and providing variety of services.
This thesis presents a passing people counting system using the method of template matching to identify whether the object moving into or out of the entrance is a pedestrian or not. Firstly, background subtraction is employed to find out moving objects. Secondly, feature matching is used to identify pedestrian and filter non-pedestrian objects in detection process. Lastly, tracking method is applied to recognize the direction of moving pedestrians, and the total numbers of two ways passing people is counted simultaneously.
Experiments were conducted on several passing people sequences. The results reveal that the proposed method can accurately and effectively detect and track pedestrians so as to successfully achieve the purpose of people counting.關鍵字(中) ★ 樣板比對
★ 行人統計
★ 距離轉換關鍵字(英) ★ template matching
★ distance transform
★ people counting論文目次 Abstract i
摘要 ii
誌謝 iii
目錄 iv
附圖目錄 vi
附表目錄 viii
第一章 緒論 1
1.1 研究動機 1
1.2 相關研究 2
1.2.1 前景物偵測與追蹤 2
1.2.2 進出人數統計系統 3
1.3 系統簡介 6
1.4 論文架構 9
第二章 前處理 10
2.1 背景建立與更新 10
2.2 邊緣偵測 11
2.2.1 傳統邊緣偵測器的比較 12
2.2.2 良好的邊緣偵測法的準則 12
2.2.3 Canny邊緣偵測器 13
第三章 行人偵測與辨識 19
3.1 前景物偵測 19
3.1.1 HSV色彩空間介紹 20
3.1.2 前景點偵測與陰影去除 22
3.1.3 前景物修補 23
3.2 連通元件運算與物體大小的限制 25
3.3 行人辨識 27
3.3.1 距離轉換 27
3.3.2 樣板資料庫的建立 29
3.3.3 執行效能的改進 31
3.3.4 樣板比對 34
第四章 行人追蹤與統計 37
4.1 行人定位 37
4.1.1 監視畫面的分割 37
4.1.2 行人資訊的表示 38
4.2 行人追蹤與統計 39
第五章 實驗結果 43
5.1 實驗環境與測試資料 43
5.2 行人辨識結果 45
5.3 行人追蹤結果 50
5.4 錯誤分析與討論 59
第六章 結論及未來方向 63
6.1 結論 63
6.2 未來方向 64
參考文獻 66參考文獻 [1] “台北市特定場所容留人數管制規則”,台北市政府消防局, http://www.tfd.gov.tw/download/download_02.php?species_id=45.
[2] T. Horprasert, D. Harwood and L.S. Davis, “A statistical approach for real-time robust background subtraction and shadow detection,” in Proc. IEEE Frame-Rate Workshop, 1999.
[3] B. Chen and Y. Lei, “Indoor and outdoor people detection and shadow suppression by exploiting HSV color information,” in Proc. IEEE Conf. Computer and Information Technology, pp.137-142, 2004.
[4] C. Stauffer and W.E.L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, VOL. 2, pp.246-252, 1999.
[5] A.J. Lipton, H. Fujiyoshi and R.S. Patil, “Moving target classification and tracking from real-time video,” in Proc. IEEE Workshop Applications of Computer Vision, pp.8-14, 1998.
[6] G. Welch and G. Bishop, “An introduction to the Kalman Filter”.
[7] A. Cavallaro, O. Steiger and T. Ebrahimi, “Tracking video objects in cluttered background,” IEEE Trans. Circuits and Systems for Video Technology, VOL. 15, NO. 4, Apr. 2005.
[8] H.T. Chen, H.H. Lin and T.L. Liu, “Multi-object tracking using dynamical graph matching,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, VOL. 2, pp.210-217, 2001.
[9] T.H. Chen and C.W. Hsu, “An automatic bi-directional passing-people counting method based on color image processing,” in Proc. IEEE Conf. Security Technology, pp.200-207, 2003.
[10] L. Snidaro, C. Micheloni and C. Chiavedale, “Video security for ambient intelligence,” IEEE Trans. Systems, Man and Cybernetics, Part A: Systems and Humans, VOL. 35, NO. 1, pp.133-144, Jan. 2005.
[11] G.P. Adriano, S.I.V. Mendoza, F.N.J. Montinola and P.C. Naval, “APeC: Automated people counting from video,” in Proc. PCSC Conf. Security and Networking, Philippine, 2005.
[12] “Tracking people using range information,” http://www.cs.ubc.ca/~rogic/projects.html, maintained by S. Rogic.
[13] G.K.H. Pang and C.K. Ng, “Automated people counting using template matching and head search,” in Proc. IEEE Conf. Mechatronics and Machine Vision in Practice, 2002.
[14] J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, VOL. 8, NO. 6, pp.679-698, Nov. 1986.
[15] D.M. Gavrila and V. Philomin, “Real-time object detection for smart vehicles,” in Proc. IEEE Conf. Computer Vision, pp.87-93, 1999.
[16] R. C. Gonzalez and R.E. Woods, 繆紹綱編譯, “Digital Image Processing 2/e,” 台灣培生教育出版股份有限公司出版,普林斯頓國際有限公司發行。指導教授 范國清(Kuo-Chin Fan) 審核日期 2006-7-6 推文 plurk
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