摘要(英) |
Smart Home is a trend, not only the younger generation, in the elderly is also increasing in popularity among. In the smart home, wireless networks, sensors and the central server plays an important role, sensors send over the wireless network is important information to the central server judgment, to achieve electrical control, control environment, security and surveillance.
Although there are many different sensors, but few cameras because the image is too large amount of data, the transmission in the wireless transmission rate is a challenge, in order to achieve this goal, we will segmentation the foreground image, and then using a special algorithm do high performance binary compression, then through the wireless transmission, the central server can restore contour of the object, and then use map information data, we can locate objects in the family.
In this paper we will use the video camera and embedded linux platform to simulate sensors with wireless communication module, and to
use the PC as a central server, to achieve the smart home features, we use 320 * 240 image size, the success of reaching more than 8fps. |
參考文獻 |
[1] R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, “Image change detection algorithms: a systematic survey,” IEEE Trans. Image Process., vol. 14, no. 3, pp. 294–307, Mar. 2005.
[2] D. A. Migliore, M. Matteucci, and M. Naccari, “View-based detection and analysis of periodic motion,” in Proc. 4th ACMInt.Workshop VideoSurveill. Sensor Netw., pp. 215–218, Oct. 2006.
[3] T. Aach and A. Kaup, “Bayesian algorithms for change detection in image sequences using Karkov random fields,” Sig. Proc: Im. Comm., 7(2): 56–61, 1995.
[4] L. Li and M. Leung, “Integrating intensity and texture differences for robust change detection,” IEEE Trans. Image Processing, vol. 11, pp. 105–112, Feb. 2002.
[5] S.-Y. Chien, S.-Y. Ma, and L.-G. Chen, “Efficient moving object segmentation algorithm using background registration technique,” IEEE Trans. Circuits Syst. Video Technol., vol. 12, no. 7, pp. 577–586, Dec. 2002.
[6] C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 246–252, 1999.
[7] O. Javed, K. Shafique, and M. Shah, “A hierarchical approach to robust background subtraction using color and gradient information,” in Proc. IEEE Workshop Motion Video Computing, pp. 22–27, Dec. 2002.
[8] Q. Zang and R. Klette, “Robust background subtraction and maintenance,” Proc. Int’l Conf. Pattern Recognition, vol. 2, pp. 90-93, 2004
[9] T.-H. Tsai, W.-T. Sheu and C.-Y. Lin, "Foreground Object Detection based on Multi-model Background Maintenance,” IEEE International Symposium on Multimedia, Taiwan, 2007.
[10] P.-M. Jodoin, M. Mignotte, and J. Konrad, “Statistical Background subtraction using spatial cues,” IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 12, pp. 1758–1763, Dec. 2007.
[11] D.-S. Lee, "Effective Gaussian Mixture Learning for Video Background Subtraction," IEEE Transactions on Pattern Analysis and Maching Intelligence, vol. 27, no. 5, MAY 2005.
[12] T.-H. Tsai, W.-T. Sheu and C.-Y. Lin, "Foreground Object Detection based on Multi-model Background Maintenance”, IEEE International Symposium on Multimedia, Taiwan, 2007.
[13] Lionel Lacassagne,Maurice Milgram, Patrick Garda, "Motion detection, labeling, data association and tracking, in real-time on RISC computer” 1Laboratoire des Instruments et Systemes Universite Pierre et Marie Curie BC 252.
[14] Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer, "Kernel-based object
tracking" IEEE Trans. Pattern Analysis and Machine Intelligence, 2003, vol. 25, no. 5, pp. 564-575.
[15] Zulfiqar Ali. Sibtul Hussain, Imtiaz A. Taj, Patrick Garda, "Kernel Based Robust Object Tracking Using Model Updates and Gaussian Pyramids” Army Public College Of Management and Sciences (APCOMS).
[16] Hong-xiang DUAN, Qiu-yu ZHANG, Wei MA, Patrick Garda, "An Approach to Dynamic Hand Gesture Modeling and Real-time Extraction” .
[17] Hao Sung, , Wen-Yan Kuo, "A Skip-line with Threshold Algorithm for Binary Image Compression” , 2010 3rd International Congress on Image and Signal Processing.
[18] Tsung-Han Tsai, Chih-Hao Chang , Chih-Chi Huang, "INTELLIGENT BIRD’S-EYE VIEW SURVEILLANCE SYSTEM FOR DUAL-CORE PLATFORM” , Conference on Computer Vision, Graphics, and Image Processing (CVGIP), Nantou, Taiwan, Aug. 2012. |