國立臺灣師範大學博碩士論文全文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	邱建中
論文名稱：	利用時空域分析與背景相減法作視訊移動物偵測 Using Temporal-spatial Analysis and Background Subtraction Method to Detect Moving Objects in the Video Sequence
指導教授：	葉榮木 Yeh, Zong-Mu 蔡俊明 Tsai, Chun-Ming
學位類別：	碩士 Master
系所名稱：	機電工程學系 Department of Mechatronic Engineering
論文出版年：	2009
畢業學年度：	97
語文別：	中文
論文頁數：	83
中文關鍵詞：	時空域分析、陰影偵測、移動物偵測、背景重建、背景相減、angle-module 色彩座標轉換
英文關鍵詞：	Temporal-spatial analysis, shadow detection, dynamic object detect, background rebuilding, background subtraction, angle-module rule
論文種類：	學術論文
相關次數：	點閱：148 下載：4
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

利用電腦視覺方式做移動物偵測時，所遭遇到最大的問題就是動態背景雜訊以及前景本身因移動而產生的雜訊，尤其在使用背景相減法作前景擷取時，這兩種雜訊更為明顯。因此，本論文提出結合前景物時域、空間域以及色彩資訊等方式來改善偵測的正確性。本方法可分為主要三個部分：(1) 利用時序統計長方圖的方式建立可隨時間更新的背景。(2) 再以angle-module方法將三維色彩資訊轉換為二維的色相變化與色彩強度資訊，利用自適應的背景相減法擷取動態前景物，運用前景與背景色彩資訊的差異性來將前景物雜訊去除（陰影、小變化雜訊）。(3) 最後結合影像時間與空間資訊的概念，來去除動態背景雜訊(例如搖曳的樹枝、雨天..等)。
實驗結果顯示，本研究的系統在室內或室外環境下都有九成以上的偵測正確率。對陰影、動態背景雜訊、以及攝影機輕微搖晃等容易造成誤判的條件下，系統也能夠有著不錯的偵測準確率。

The critical issues of motion detection based on computer vision are the noises in the dynamic background and the noises from objects’ moving in the foreground. These two noises are more obvious, especially at using background subtraction method. In this study, A method that combined with temporal-spatial and color information is used to improve the detection accuracy. The method can be divided into three sections: (1) The time-varying updated background is built by temporal statistic histogram; (2) Three dimension color information is transferred into two dimension color phase and color intensity by angle-module rule. Next, moving objects in the foreground are extracted by adaptive background subtraction, and the noises (shadows and small change) are removed according to variations of color information in the background and foreground; (3) Dynamic background noises (ex: branches movements and rain interferences) are removed by the concept combined with temporal and spatial information of video sequences.
As the results present, our accuracy of the detection is upper than ninety percentage in the outside and inside environments. The system also has good performance when the false detection is caused by shadows, dynamic background noises, and camera shakings.

誌謝............................................I
摘要...........................................II
Abstract......................................III
目錄...........................................IV
圖目錄.........................................VI
表目錄.........................................IX
第一章 緒論......................................1
1-1 前言........................................1
    1-2 研究動機與目的...........................2
    1-3 本文所提到的方法.........................4
    1-4 論文架構................................5
第二章 文獻探討與回顧..............................6
2-1 相關基本理論介紹..............................6
2-1-1 色彩空間...................................6
2-1-2 中值濾波器.................................10
2-1-3 形態學運算.................................12
    2-2 相關研究.................................17
2-2-1 文獻探討...................................20
2-2-2 綜合討論...................................26
第三章  前景移動物偵測系統.........................28
3-1 系統功能與架構................................28
    3-2 影像縮減.................................30
    3-3 背景重建與更新............................32
3-3-1 背景重建...................................32
3-3-2 背景更新...................................36
3-4  自適應性閥值的移動前景物擷取...................37
    3-5  前景雜訊去...............................41
3-5-1 陰影去除....................................41
3-5-2 微小雜訊去除.................................43
    3-6 動態背景雜訊去除...........................45
    3-7 擷取影像的修補與復原........................50
第四章 實驗與分析...................................52
    4-1 室內移動物偵測實驗..........................53
       4-1-1 實驗結果..............................53
       4-1-2 實驗討論..............................58
4-2 室外移動物偵測實驗..............................63
4-2-1 實驗結果.....................................63
   4-2-2 實驗討論..................................67
4-3 攝影機晃動下的移動物偵測實驗......................70
4-3-1 實驗結果.....................................70
   4-3-2 實驗討論..................................72
4-4 綜合分析.......................................74
第五章 結論.........................................79
參考文獻...........................................81
                                

1. Wikipedia: public editor, “RGB color model”,
available at:
http://en.wikipedia.org/wiki/RGB.
2. Colantoni, P., “Cours-Couleur.Org”,
available at:
http://www.couleur.org/index.php?page=transformations
3. 鐘國亮，「影像處理與電腦視覺」，台灣東華書局股份有限公司，2006
4. Lipton, A.J., Fujiyoshi, H., and Patil, R.S.,
“Moving Target Classification and Tracking from Real-
time Video”,IEEE Workshop on Applications of Computer
Vision, pp. 8-14, 1998.
5. Wixson, L., and Hansen, M., “Detecting Salient Motion
by Accumulating Directionally-consistent Flow”, IEEE
Transactions on Pattern Analysis and Machine
Intelligence,vol. 22, pp. 774-780, 2000.
6. Tian, Y.L., and Hampapur, A., “Robust Salient Motion
Detection with Complex Background for Real-time Video
Surveillance”,IEEE Workshop on Motion and Video
Computing, vol. 2, pp. 30-35, 2005.
7. Huang, K., Wang, L., Tan, T., and Steve, M., “A Real-
time ObjectDetecting and Tracking System for Outdoor
Night Surveillance”,Pattern Recognition, vol. 41, pp.
432-444, 2008.
8. Wren, C., Azarbayejani, A., Darrel, T., and Pentland,
A.P.,“Pfinder: Real-time Tracking of Human Body”,
IEEE Transactionson Pattern Analysis and Machine
Intelligence, vol. 19, pp. 780-785, 1997.
9. Koller, D., Weber, J., Hung, T., Malik, J., Ogasawara,
G., Rao, B.,and Russel, S., “Towards Robust Automatic
Traffic Scene Analysis in Real-time”,Computer Vision &
Image Processing, vol. 1, pp. 126-131, 1994.
10. Haritaoglu, I., Harwood, D., and Davis, L.S., “W4:
Real-time Surveillance of People and Their
activities”, IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 22, pp. 809-830, 2000.
11. Stauffer, C., and Grimson, W.E.L., “Adaptive
Background Mixture Models for Real-time Tracking”,
Computer Vision and Pattern Recognition, vol. 2, pp.
246-252, 1999.
12. Elgammal, A., Harwood, D., and Davis, L.S., “Non-
Parametric Model for Background Subtraction”, Computer
Vision ECCV 2000, pp. 751-764, 2000.
13. Ekinci, M., and Gedikli, E., “Silhouette Based Human
Motion Detection and Analysis for Real-time Automated
Video Surveillance”, Turkish Journal of Electrical
Engineering & Computer Sciences, vol.13, pp. 199-229,
2005.
14. Kumar, P., Sengupta, K., and Lee, A., “A Comparative
Study of DifferentColor Spaces for Foreground and
Shadow Detection for Traffic Monitoring System”, The
IEEE 5th International Conference on Intelligent
Transportation Systems, pp. 100-105, 2002.
15. Enrique, J., Martínez, J., and Mira, J., “A new Video
Segmentation Method of Moving Objects Based on Blob-
Level Knowledge”, Pattern Recognition Letters, vol.
29, pp. 272-285, 2008.
16. Jabri, S., Duric, Z., and Wechsler, H., Tracking Groups
of People”, Computer Vision and Image Understanding,
vol. 80, pp. 42-56, 2000.
17. Javed, O., Shafique, K., and Shah, M., “A Hierarchical
Approach to Robust Background Subtraction Using Color
and Gradient Information”, Workshop on Motion and
Video Computing, pp. 22-27, 2002.
18. Wardhani, A., and Thomson, T., “Content Based Image
Retrieval Using Category-based Indexing”, 2004 IEEE
International Conference on Multimedia and Expo (ICME),
vol. 2, pp. 783-786, 2004.
19. Doshi, A., “highwayI”, database available at:
http://cvrr.ucsd.edu/aton/shadow/index.html.
20. 李振(金昇)，「非靜態背景條件下之移動性人物偵測」，國立中正大學
電機工程研究所碩士論文，民國96年7月。
21. Tai, J.C., and Song, K.T., “Background Segmentation
and Its Application to Traffic Monitoring Using
Modified Histogram”, IEEE International Conference on
Networking, Sensing and Control, vol. 1, pp. 13-18,2004.
22. Prati, A., Mikic, I., Trivedi, M.M., and Cucchiara,
R., “Detecting Moving Shadows: Algorithms and
Evaluation”, IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol.25, pp. 918-923, 2003.
23. Prati, A., Mikic, I., Crana, C., Trivedi,
M.M., “Shadow Detection Algorithms for Traffic Flow
Analysis: a Comparative Study”, IEEE Intelligent
Transportation Systems Conference Proceedings, pp. 25-
29, 2001.
24. Spagnolo, P., OrazioM, T.D., Leo, M., and Distante,
A., “Moving Object Segmentation by Background
Subtraction and Temporal Analysis”, Image and Vision
Computing, vol. 24, pp. 411-423, 2006.
25. Li, G., Wang, Y., and Shu, W., “Real-time Moving
Object Detection for Video Monitoring Systems”, Second
International Symposium on Intelligent Information
Technology Application, vol. 3, pp. 163-166, 2008.
26. Sijun, L., Jian, Z., Feng, D., “An Efficient Method
for Detecting Ghost and Left Objects in Surveillance
Video”, IEEE Conference on Advanced Video and Signal
Based Surveillance, pp.540-545, 2007.
27. Fisher, R., “Browse1”, database available at:
http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/.
28. Liyuan, L., “Campus with wavering tree branches”,
database available at: http://perception.i2r.a-
star.edu.sg/bk_model/bk_index.html.

簡易檢索 / 詳目顯示

相關論文