Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/9176
標題: 分散式複眼監控系統
A Video Surveillance System Based on the Distributed Ommateum
作者: 普達
Gabin, KPODA
關鍵字: OpenCV
OpenCV
移動物體偵測
監控系統
Webcam協調演算法
Moving Object-Tracking
Surveillance System
Webcam Coordination algorithms
出版社: 電機工程學系所
引用: 1. Valera, M. and S.A. Velastin, Intelligent distributed surveillance systems: a review. Vision, Image and Signal Processing, IEE Proceedings -, 2005. 152(2): p. 192-204. 2. Tao, L., Z. Haihe, and L. Xiujuan. Embedded video monitoring system on ARM and linux. in Electrical and Control Engineering (ICECE), 2011 International Conference on. 2011. 3. Wang, J. and H. He. ARM-based embedded video monitoring system research. in Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on. 2010. 4. Fang Mei, X.S., Haipeng Chen, Yingda Lv, Embedded Remote Video Surveillance System Based on ARM, in Control Engineering and Applied Informatics (CEAI)2011. p. 51-57. 5. Ying-Wen, B., et al. Design and implementation of an embedded surveillance system with video streaming recording triggered by an infrared sensor circuit. in Communications and Information Technologies, 2007. ISCIT ''07. International Symposium on. 2007. 6. Cui, B., J. Cui, and Y. Duan. Intelligent Security Video Surveillance System Based on DaVinci Technology. in Measuring Technology and Mechatronics Automation (ICMTMA), 2013 Fifth International Conference on. 2013. 7. Fei, Z., et al. Embedded intelligent video surveillance and cooperative tracking system. in Communications and Networking in China (CHINACOM), 2012 7th International ICST Conference on. 2012. 8. Meng, L., C. Wu, and Z. Yunzhou. A review of Traffic Visual Tracking technology. in Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on. 2008. 9. Sandeep Kumar Patel, A.M., Moving Object Tracking Techniques: A Critical Review. Indian Journal of Computer Science and Engineering (IJCSE), 2013. 4(2): p. 95-102. 10. P.Suresh, J.J.A., Systematic Survey on Object Tracking Methods in Video. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2012. 1(8): p. 242-247. 11. Gang, X., et al. Moving target tracking based on adaptive background subtraction and improved camshift algorithm. in Audio, Language and Image Processing (ICALIP), 2012 International Conference on. 2012. 12. Wang, X. and X. Li. The study of MovingTarget tracking based on Kalman-CamShift in the video. in Information Science and Engineering (ICISE), 2010 2nd International Conference on. 2010. 13. Shengluan, H. and H. Jingxin. Moving object tracking system based on camshift and Kalman filter. in Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on. 2011. 14. Sobral, A., {BGSLibrary}: An OpenCV C++ Background Subtraction Library, 2013: IX Workshop de Visao Computacional (WVC''2013). 15. JAMMOUSSI, A.S.A.Y., Object tracking system using Camshift, Meanshift and Kalman filter. World Academy of Science, Engineering and Technology 2012(64): p. 674-679. 16. Gary Bradski and Adrian Kaehler, Learning OpenCV. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. September 2008: First Edition. 17. Valera, M.; Velastin, S.A., "Real-time architecture for a large distributed surveillance system," Intelligent Distributed Surveilliance Systems, IEE , vol., no., pp.41,45, 23 Feb. 2004 18. Donahoo, M.J. and K.L. Calvert, TCP/IP Sockets in C: Practical Guide for Programmers. 2009: Elsevier Science. 19. Donahoo, M.J. and K.L. Calvert, TCP/IP Sockets in C: Practical Guide for Programmers. 2002: Elsevier Science. 20. Stevens, W.R., B. Fenner, and A.M. Rudoff, UNIX Network Programming. 2004: Addison Wesley Professional. 21. Comer, D. and D.L. Stevens, Internetworking with TCP/IP.: Client-server programming and applications. 2001: Prentice Hall. 22. OpenCV Official Website, http://code.opencv.org/projects/opencv/wiki, 23. http://fr.slideshare.net/helloansuman/installing-open-cv-245 24. http://www.mkmoharana.com/2012/01/setting-up-opencv-231-on-visual-studio.html 25. Linux in Embedded Systems, http://elinux.org/BeagleBoardUbuntu 26. http://www.brianhensley.net/2013/01/beagleboard-xm-how-to-install-ubuntu.html 27. OpenCV on Ubuntu, http://docs.opencv.org/doc/tutorials/introduction/linux_install/linux_install.html 28. http://karytech.blogspot.tw/2012/05/opencv-24-on-ubuntu-1204.html 29. http://miloq.blogspot.tw/2012/12/install-opencv-ubuntu-linux.html 30. CodeBlocks and OpenCV on Ubuntu, http://digitus.itk.ppke.hu/~losda/anyagok/OpenCV/CodeBlocks_OpenCV.pdf 31. Record Desktop using ffmpeg on Ubuntu, http://wiki.oz9aec.net/index.php/High_quality_screen_capture_with_Ffmpeg 32. http://www.wikihow.com/Record-Your-Desktop-Using-FFmpeg-on-Ubuntu-Linux 33. Record Desktop using vlc on Windows, http://www.howtogeek.com/120202/how-to-record-your-desktop-to-a-file-or-stream-it-over-the-internet-with-vlc/
摘要: This work deal with the design of distributed video surveillance system. Our purpose is to realize an embedded video surveillance system capable of video streaming back to a remote server, detecting and tracking any moving object using pan-tilt cameras. The constructed system should be perfectly monitorable from the remote server. We can say that we have achieved this goal by using embedded technology, socket programing, computer vision resources (algorithms) and distributed system design method. The algorithm used for detection is a background subtraction technique namely temporal frame difference. To be able to locate and track the detected object we made use of camshift algorithm. The built system is composed of four client boards and one server PC. Client’s subsystem is based on an embedded processor (Beagleboard xM) and an embedded microcontroller (MSP430F5438). A pan-tilt camera is also part of each client’s subsystem. From the remote server we make use of one two or three cameras to detect and track the same target. We also can form tracking groups each composed of two cameras to track the two different targets. So, the four cameras are considered as a compound eye of our surveillance system. The major contribution of this work is without doubt the cooperative tracking technique we have implemented. In fact, by using a leader election algorithm, we elect a leader. This leader will then select it direct neighbors (followers) and get them involved in the tracking process. With this approach we get a good cooperative tracking results. Moreover the pan–tilt system automation provide a multi-view of the monitoring scene. At last the embedded technology devices we chose give our system good computation capabilities, mobility and real-time ability.
URI: http://hdl.handle.net/11455/9176
其他識別: U0005-1207201312234900
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1207201312234900
Appears in Collections:電機工程學系所

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