跳到主要內容

臺灣博碩士論文加值系統

(34.236.36.94) 您好!臺灣時間:2021/07/24 21:09
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:陳俊宏
研究生(外文):Chun-Hung Chen
論文名稱:以彩色影像技術為基礎的動態物件追蹤系統
論文名稱(外文):Motion Object Tracking System Using Color-based Techniques
指導教授:高成炎高成炎引用關係
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:97
語文別:英文
論文頁數:55
中文關鍵詞:視訊監控背景模擬前景偵測移動物件追蹤物件遮蔽處理
外文關鍵詞:video surveillancesbackground modelingforeground extractioncontrast histogramobject trackingocclusion detection
相關次數:
  • 被引用被引用:0
  • 點閱點閱:183
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
在多媒體處理的領域裡,實際環境或影片中物件偵測與追蹤是近年來新興的研究主題。許多相關的研究被提出以滿足特定環境的條件。隨著近幾年監控系統的興盛發展,具有人工智慧的視訊監控系統逐漸變成能紀錄人們ㄧ舉一動的熱門產品。在法庭上監視系統所記錄的影片可以是使犯人伏法的決定證據。為了建構出有效的監控系統,我們必須對每一個環節都要有ㄧ定的了解,所有使用的技術都要符合實際的要求,例如演算法的複雜度不能太高。如何快速而有效的計算是本實驗相當棘手的問題。我們提出或變更其他人以彩色影像為基礎的方法,其中包括背景和物件模型建構與更新、前景偵測、移動物件偵測與追蹤。如果採用多種不同基礎的方法,像是以形狀、線條與特徵點的技術,將會增加許多計算的時間。實驗的場景包括室內外,我們的系統能夠在這些場景中精確地追蹤移動物件並且有效的處理物件遮蔽的問題。
Motion object tracking in real-time environments and videos is a popular topic in multimedia processing. Various related researches are proposed to handle particular cases in recent years. With the flourish of surveillance systems in the world, intelligent video surveillance systems became popular products to record activities of human. A video can be an evidence to guarantee someone as suspect in courts. To develop a robust tracking system we have to take care every part of this system, all techniques about image processing must meet our requirements like fast computation and adapting to dynamic environments. On-line computation is a critical problem to our algorithms. We proposed several modified color-based methods about background modeling, foreground detection, motion object modeling and matching to achieve the goal that tracking multiple objects in indoor and outdoor scenarios. If we adapt and propose multiple techniques of distinct bases such as shape, edge and feature point, it must take much more time than our system. In experimental settings, we can discriminate and track objects accurately as well as detect and deal with occlusions in all videos.
致謝……………………………………………………………………………....…iii
中文摘要……………………………………………………………………....…....iv
Abstract…………………………………………………………………………....…v
1 Introduction…………………………………………………................................1
1.1 Background………………………………………………………………...1
1.2 System Overview…………………………………………………………..2
1.3 Related Works……………………………………………………………..3
1.4 Thesis Organization………………………………………………….…11
2 Preprocessing work…………………………………………………………....13
2.1 Background Model Construction…………………………………….…13
2.2 Foreground Extraction……………………………………………….…15
2.2.1 Method 1………………………………………………………..…16
2.2.2 Method 2………………………………………………………..…16
2.3 Background Model Update…………………………………………….…20
3 Object Recognition and Tracking…………………………………………..…22
3.1 Global Color Similarity Comparison……………………………………22
3.2 Detailed Information Comparison………………………………….…...25
3.3 Occlusion Detection………………………………………………….…29
4 Experimental Results……………………………………………………….....35
4.1 Indoor Human Tracking…………………………………………….…..35
4.2 Outdoor Human Tracking………………………………………….........39
4.3 Vehicle Tracking………………………………………………….…….44
4.4 Summary………………………………………………………….…….47
5 Conclusion……….……………………………………………………………...50
5.1 Conclusion…………………………………………………………….....50
5.2 Future Work………………………………………………………………50
Bibliography……………………………………………………………………...….52
[1] Shao-Yi Chien, Shyh-Yih Ma, Liang-Gee Chen, Efficient moving object segmentation algorithm using background registration technique, IEEE Transanction on Circuits and Systems for Video Technology, Vol. 12, Issue 7, 2002, pp. 577-586.
[2] Kentaro Toyama, John Krumm, Barry Brumitt, Brian Meyers, Wallflower: Principles and practice of background maintenance, The Proceeding of the seventh IEEE International Conference on Computer Vision, Vol. 1, 1999, pp.255-261.
[3] Chris Stauffer W.E.L Grimson, Adaptive background mixture models for real-time tracking, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 1999, pp. 246-252.
[4] Paul Viola, Michael Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, Vol. 57, No.2, 2004, pp.137-154.
[5] Paul Viola, Michael Jones, Detecting Pedestrians Using Patterns of Motion and Appearance, International Journal of Computer Vision, Vol.63, No.2, pp153-161, 2005.
[6] Junqiu Wang, and Yasushi Yagi, Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking, Vol. 17, Issue 2, 2008, pp. 235-240.
[7] Dorin Comaniciu and Peter Meer, Mean shift : a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, Issue 5, 2002, pp. 603–619.
[8] Kaiqi Huang, Liangsheng Wang, Tieniu Tan, Steve Maybank, A Real-time object detection and tracking system for outdoor night, Science Direct-Pattern recognition, Vol. 41, issue 1, 2008, pp. 432-444.
[9] Thanarat Horprasert, David Harwood, and Larry S. Davis, A Robust Background Subtraction and Shadow Detection, Proceedings of 4th Asian Conference on Computer Vision, 2000, pp. 983-988.
[10] Tao Yang, Stan Z.Li, Quan Pan, Jing Li, Real-time and accurate segmentation of moving objects in dynamic scene, Proceedings of the ACM 2nd international workshop on Video surveillance and sensor networks, 2004, pp. 136-143.
[11] Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer, Kernel-based object tracking, IEEE Transition on Pattern Analysis and Machine Intelligence, vol. 25, issue 5, 2003, pp. 564-577.
[12] Lin Zhu, Jie Zhou, Jingyan Song, Tracking multiple objects through occlusion with online sampling and position estimation, Science Direct-Pattern recognition, Vol. 41, Issue 8, 2008, pp. 2447-2460.
[13] C. Huang, C. Chen, and P. Chung. Contrast Context Histogram–A Discriminating Local Descriptor for Image Matching. International Conference on Pattern Recognition, Vol. 4, 2006, pp. 53–56.
[14] http://visualsurveillance.org/PETS2001
[15] 蔡博智, A Study of Monitoring and Control System Using Image Tracking Method, 中原大學機械工程學系碩士論文, 2002.
[16] Yu-Ting Chen, Chu-Song Chen, Chun-Rong Huang, Yi-Ping Hung, Efficient hierarchical method for background subtraction, Science Direct-Pattern recognition, volume 39, 2006, pp. 2706-2715.
[17] Jundi Ding, Runing Ma, and Songcan Chen, A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation, IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 17, No. 2, 2008.
[18] Yi-Ta Wu, Frank Y. Shih, Jiazheng Shi, Yih-Tyng Wu, A top-down region dividing approach for image segmentation, Science Direct-Pattern recognition, volume 41, issue 6, 2008, pp. 1948-1960.
[19] M. D. Huang and L. H. Chen, Two New Surveillance Systems, proceeding of the 15th IPPR Conference on CVGIP, Taiwan, 2002.
[20] 陳昭介, 門禁監控系統之影像追蹤, 中央大學電機工程學系碩士論文, 2004.
[21] 劉秋宗, Intruder Detection with Moving Cameras, 台灣大學資訊工程學系碩士論文, 2008.
[22] Hanzi Wang and David Suter, A consensus-based method for tracking:Modelling background scenario and foreground appearance, Science Direct-Pattern recognition, volume 40, 2007, pp.1091-1105.
[23] Navneet Dalal and Bill Triggs, Histograms of oriented gradients for human detection, IEEE computer society conference on Computer Vision and Pattern Recognition, vol. 1, 2005, pp. 886-893.
[24] Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, second edition, 2001, Prentice Hall.
[25] S. J. McKenna, S. Jabri, Z. Duric, and A. Rosenfeld, Tracking groups of people, Computer Vision and Image Understanding, No. 80, 2000, pp. 42-56.
[26] Katja Nummiaro, Esther Koller-Meier, Tom′aˇs Svoboda,Daniel Roth, and Luc Van Gool, Color-Based Object Tracking in Multi-Camera Environments, Lecture Notes in Computer Science, Volume 2781, 2003.
[27] B. Jhne, H. Scharr, and S. Krkel, Principles of filter design, in Handbook of Computer Vision and Applications, B. Jhne, H. Hauecker, and P. Geiler, Eds. New York: Academic, 1999, vol. 2, pp. 125–151.
[28] Michael Mason and Zoran Duric, using histograms to detect and track objects in color video, Applied imagery pattern recognition workshop, AIPR 30th, 2001, pp. 154-159.
[29] Dar-Shyang Lee, Effective Gaussian Mixture Learning for Video Background Subtraction, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, Issue 5, 2005, pp. 827–832.
[30] http://media.xiph.org/video/derf/
[31] Bangjun Lei and Li-Qun Xu, Real-time outdoor video surveillance with robust foreground extraction and object tracking via multi-state transition management, Science Direct-Pattern recognition letters, volume 27, 2006, pp.1816-1825.
[32] Charay Lerdsudwichai, Mohamed Abdel-Mottaleb, and A-Nasser Ansari, Tracking multiple people with recovery from partial and total occlusion, Science Direct-Pattern recognition, volume 38, 2005, pp.1059-1070.
[33] http://www.iii.org.tw/
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top