使用動態背景補償以偵測與追蹤移動監控畫面之前景物

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：49

、訪客IP：3.135.184.255

姓名

馬翔毅(Hsung-yi Ma) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

使用動態背景補償以偵測與追蹤移動監控畫面之前景物
(Object Detection and Tracking for a Moving Surveillance Camera by Using Dynamic Background Compensation)

相關論文

★ 基於QT之跨平台無線心率分析系統實現	★ 網路電話之額外訊息傳輸機制
★ 針對與運動比賽精彩畫面相關串場效果之偵測	★ 植基於向量量化之視訊/影像內容驗證技術
★ 植基於串場效果偵測與內容分析之棒球比賽精華擷取系統	★ 以視覺特徵擷取為基礎之影像視訊內容認證技術
★ 應用於H.264/AVC視訊內容認證之適應式數位浮水印	★ 棒球比賽精華片段擷取分類系統
★ 利用H.264/AVC特徵之多攝影機即時追蹤系統	★ 利用隱式型態模式之高速公路前車偵測機制
★ 基於時間域與空間域特徵擷取之影片複製偵測機制	★ 結合數位浮水印與興趣區域位元率控制之車行視訊編碼
★ 應用於數位智權管理之H.264/AVC視訊加解密暨數位浮水印機制	★ 基於文字與主播偵測之新聞視訊分析系統
★ 植基於數位浮水印之H.264/AVC視訊內容驗證機制	★ 利用隱式型態模式之自適應車行監控畫面分析系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

自動化監控是近年來熱門的研究方向，由於監控人員無法永遠專注地監視攝影畫面，利用自動化監控來幫忙追蹤監視是必要的。但目前的監控系統是以固定式定點拍攝的攝影機為主要設備，由於攝影範圍有限，因而會有許多的死角，造成監視上的困難。因此，我們採用可旋轉式攝影機(Pan-Tilt-Zoom camera)，利用其可控制移轉的特性來增加監控視野。
本研究提出動態背景預測法來偵測並追蹤物體。當攝影機未移動時，利用背景相減找出移動物體並取出其特徵點，在攝影機追蹤移動時，採用光流運算得到移動物體的特徵點移動後的位置與背景移動向量，進而估計出當前的背景影像，再用動態背景補償的方式，防止預測誤差傳播擴散，最後採用分水嶺演算法得到物體更精確的輪廓，以控制攝影機完成追蹤。
當移動物體突然轉向或停止時，我們所提出的方法依然可以正確的追蹤物體，實驗結果證實我們所提出方式的可行性。

摘要(英)

There are increasing demands to detect usual/unusual events in various environments nowadays. Deploying cameras in public/private areas to form a ubiquitous surveillance system is thought to be very helpful in ensuring safety of people in many aspects. However, as more and more cameras are being installed, it may become impractical and cumbersome to find available human resources to achieve effective surveillance. Advanced surveillance systems that can actively monitor an area/object of interest and automatically identify abnormal situations are considered to be a promising solution. The advanced surveillance systems rely on analyzing the visual data recorded by the cameras to determine if unusual events happen. The issue of object tracking in video frames is thus very important and needs to be investigated thoroughly.
In this research, we adopt Pan-Tilt-Zoom (PTZ) cameras in our surveillance environment and propose a novel detection and tracking algorithm for dynamic scene videos captured by a PTZ camera. In our system, we first use the static scene tracking algorithm to construct the background and then use the dynamic scene tracking algorithm when the camera starts moving. The optical flow approach is used to detect the background motion and then predict the current background image. The background subtraction is then applied to obtain the rough foreground regions. In order to better predict the next frame, we compensate the predicted background to prevent error propagation. Finally, the watershed algorithm is applied to obtain a more precise contour of the foreground object. The camera is controlled to move for tracking the object accordingly. Experimental results show the feasibility of the proposed system.

關鍵字(中)

★ 物體追蹤
★ 監視系統

關鍵字(英)

★ visual surveillance system
★ object tracking

論文目次

第一章緒論 1
1-1 研究背景目的與貢獻 1
1-2 論文架構 2
第二章現存前景物體偵測與追蹤方式 3
2-1 現存的靜態影像追蹤方式 3
2-1-1 前景物體偵測 3
2-1-2 追蹤方式 7
2-2 現存的動態影像追蹤方式 9
2-2-1 建立半球狀全景背景影像 9
2-2-2 使用光流運算來追蹤 10
2-2-3 搭配場景攝影機定位協助追蹤移動物體 11
第三章使用動態背景補償以偵測與追蹤移動監控畫面之前景物 12
3-1 系統簡介 12
3-2 特徵點的偵測與追蹤 14
3-2-1 特徵點的偵測 14
3-2-2 特徵點的分類 15
3-3 動態背景預估法 16
3-3-1 偵測背景移動 17
3-3-2 向量權重分類器 18
3-3-3 動態背景生成 19
3-3-4 修正背景預測的誤差 21
3-4 動態背景補償 24
3-5 利用分水嶺演算法求得前景輪廓 27
3-6 光流誤差修正 30
3-6-1 過濾光流運算的錯誤 30
3-6-2 特徵點集合的更新 33
3-7 PTZ攝影機控制 33
3-8 系統流程 36
第四章實驗結果 37
第五章結論與未來研究方向 39
5-1 結論 39
5-2 未來研究方向 40
第六章參考文獻 41

參考文獻

[1] D. Meyer, J. Denzler, and H. Niemann, “Model based extraction of articulated objects in image sequences for gait analysis,” in Proc. IEEE International Conference on Image Processing, 1998, pp. 78–81.
[2] J. Barron, D. Fleet, and S. Beauchemin, “Performance of optical flow techniques,” International journal of computer vision, vol. 12, no. 1, pp. 42–77, 1994.
[3] C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland, “Pfinder: real-time tracking of the human body,” IEEE Trans. Pattern Analysis Machine Intelligence, vol. 19, pp. 780–785, July 1997.
[4] Kalman, R. E. "A new approach to linear filtering and prediction problems," Transactions of the ASME - Journal of Basic Engineering Vol. 82: pp. 35-45 (1960).
[5] C. A. Pau and A. Barber, “Traffic sensor using a color vision method,” in Proc. SPIE—Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS, vol. 2902, 1996, pp. 156–165.
[6] B. Schiele, “Vodel-free tracking of cars and people based on color regions,” in Proc. IEEE Int. Workshop Performance Evaluation of Tracking and Surveillance, Grenoble, France, 2000, pp. 61–71.
[7] R. Polana and R. Nelson, “Low level recognition of human motion,”in Proc. IEEE Workshop Motion of Non-Rigid and Articulated Objects, Austin, TX, 1994, pp. 77–82.
[8] B. Coifman, D. Beymer, P.McLauchlan, and J. Malik, “Areal-time computer vision system for vehicle tracking and traffic surveillance,” Transportation Res.: Part C, vol. 6, no. 4, pp. 271–288, 1998.
[9] J. Malik and S. Russell, “Traffic surveillance and detection technology development (new traffic sensor technology),” Univ. of California, Berkeley, 1996.
[10] A. Baumberg and D. C. Hogg, “Learning deformable models for tracking the human body,” in Motion-Based Recognition, Eds. Norwell, MA: Kluwer, 1996, pp. 39–60.
[11] C. Micheloni, G.L. Foresti, “Zoom on target while tracking”, IEEE International Conference on Image Processing (ICIP), 2005.
[12] Chih-Chiang Hu, Chin-Chuan Han, Jun-Wei Hsieh, and Kuo-Chin Fan, “Construction of panoramic images for monitoring environment,” 17th IPPR Conference on Computer Vision, Graphics, and Image Processing, 2004.
[13] S. Coorg and S. Teller, “Spherical mosaic with quaternions and dense correlation,” International Journal of Computer Vision, vol. 37,pp. 259–273, 2000.
[14] E. Noirfalise, J. Lapreste, F. Jurie, and M. Dhome, “Real-time registration for image mosaicing,” in Proc. of British Machine Vision Conference, 2002.
[15] M. Greiffenhagen, D. Comaniciu, H. Neimann and V. Ramesh. “ Design, analysis, and engineering of video monitoring systems: An Approach and a case study,” in Proceedings of IEEE, vol.89, no.10, October, pp.1498-1517, 2001.
[16] N. Peterfreund, “Robust tracking of position and velocity with Kalman snakes,”IEEE Trans. Pattern Analysis Machine Intelligence, vol. 22, pp. 564–569, June 2000.
[17] A. Elgammal, et al., “Background and foreground modeling using non-parametric kernel density estimation for visual surveillance,” Proceedings of the IEEE. 90(7): p. 1151-1163, 2002.
[18] Manoj Aggarwal, Rakesh Kumar, Harpreet Sawhney. “Real-time wide area multi-camera stereo tracking,“ IEEE Computer Society Conference on Vision and Pattern Recognition, 2005.
[19] Ting-Hsun Chang, Shaogang Gong. ”Tracking multiple people with a multi-camera system”. IEEE Workshop on Multi-Object Tracking, 2001.
[20] Hanzi Wang and David Suter. “Tracking and segmenting people with occlusions by a sample consensus based method”, IEEE, 2005.
[21] Omar Javed, Mubarak Shah. “KNIGHTM: A multi-camera Surveillance System”, 2003.
[22] S. Khan, O. Javed, Z. Rasheed and M. Shah, “Human tracking in multiple camera”, Proc. IEEE Computer Vision, 2001.
[23] T Horprasert, D Harwood, LS Davis. “A statistical approach for real-time robust background subtraction and shadow detection”, IEEE ICCV.
[24] Dockstader, S.L., Tekalp, A.M. “Multiple camera fusion for multi-object tracking”, IEEE Workshop on Multi-Object Tracking, 2001.
[25] Don Murray and Anup Basu, ”Motion Tracking with an Active Camera”, IEEE Trans. Pattern Analysis Machine Intelligence, vol. 16, no. 5, May 1994.
[26] Y. C. Chung, C. H. Wang, J. M. Wang, S. C. Lin, S. W. Chen, “Integration of Omnidirectional and Movable Cameras for Indoor Surveillance”, IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP), 2004.
[27] H. P. Moravec, “Towards automatic visual obstacle avoidance,” in Proc. 5th International Joint Conference on Artificial Intelligence, pp.584 1977.

指導教授

蘇柏齊(Po-Chyi Su)

審核日期

2007-7-20

推文