• Mayssaa Al Najjar
  • Milad Ghantous
  • Magdy Bayoumi
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 114)


Surveillance systems are considered an important technological tool for monitoring environments of interest and detecting malicious activities. These systems are receiving a growing attention for security and safety concerns. With the advances in imaging and wireless technology, tiny visual sensor nodes are employed to collectively monitor areas of interest. These nodes are capable of capturing and processing images, and intelligently sending just the right amount of data to the central station for further activity interpretation. However, constrained resources of these sensor platforms raise new challenges for video surveillance. This chapter presents an overview of surveillance systems, applications, evolution, and challenges. It then summarizes the motivations, contributions, and organization of the rest of the book.


Surveillance System Image Fusion Object Detection Image Processing Algorithm Embed Platform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    W. Hu, T. Tan, L. Wang and S. Maybank, "A survey on visual surveillance of object motion and behaviors," IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, vol. 34, no. 3, pp. 334-352, 2004.CrossRefGoogle Scholar
  2. 2.
    R. T. Collins, A. J. Lipton and T. Kanade, "Introduction to the special section on video surveillance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 745-746, 2000.CrossRefGoogle Scholar
  3. 3.
    M. H. Sedky, M. Moniri and C. Chibelushi, "Classification of smart video surveillance systems for commercial applications," in IEEE conference on Advanced Video and Signal Based Surveillance, 2005.Google Scholar
  4. 4.
    M. Valera and S. A. Velastin, "Intelligent distributed surveillance systems: a review," IEEE Proceedings Vision, Image and Signal Processing, vol. 152, no. 2, pp. 192-204, April 2005.CrossRefGoogle Scholar
  5. 5.
    F. Helten and B. Fisher, "Video surveillance on demand for various purposes?," in B. I. F. S. Research, 2003.Google Scholar
  6. 6.
    D. Beymer, P. McLauchlan, B. Coifman and J. Malik, "A real-time computer vision system for measuring traffic parameters," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997.Google Scholar
  7. 7.
    Y.-K. Ki and D.-K. Baik, "Model for accurate speed measurement using double-loop detectors," IEEE Transactions on Vehicular Technology, vol. 55, no. 4, pp. 1094-1101, 2006.CrossRefGoogle Scholar
  8. 8.
    C. Micheloni, G. L. Foresti and L. Snidaro, "A co-operative multicamera system for video-surveillance of parking lots," in IEEE Symposium on Intelligent Distributed Surveillance Systems, London, 2003.Google Scholar
  9. 9.
    D. M. Sheen, D. L. McMakin and T. E. Hall, "Three-dimensional millimeter-wave imaging for concealed weapon detection," IEEE Transactions on Microwave Theory and Techniques, vol. 49, no. 9, pp. 1581-1592, 2001.CrossRefGoogle Scholar
  10. 10.
    G. Barrenetxea, F. Ingelrest, G. Schaefer and M. Vetterli, "Wireless sensor networks for environmental monitoring: the SensorScope experience," in IEEE International Zurich Seminar on Communications, Zurich, 2008.Google Scholar
  11. 11.
    T. H. Chen, P. H. Wu and Y. C. Chiou, "An early fire-detection method based on image processing," in IEEE International Conference on Image Processing, Singapore, 2004.Google Scholar
  12. 12.
    L. Cutrona, W. Vivian, E. Leith and G. Hall, "A high-resolution radar combat-surveillance system," IRE Transactions on Military Electronics, Vols. MIL-5, no. 2, pp. 127-131, 2009.CrossRefGoogle Scholar
  13. 13.
    M. Skolnik, G. Linde and K. Meads, "Senrad: an advanced wideband air-surveillance radar," IEEE Transactions on Aerospace and Electronic Systems, vol. 37, no. 4, pp. 1163-1175, 2001.CrossRefGoogle Scholar
  14. 14.
    J. Wang, C. Qimei, Z. De and B. Houjie, "Embedded wireless video surveillance system for vehicle," in International Conference on Telecommunications, Chengdu, China, 2006.Google Scholar
  15. 15.
    S. Fleck and W. Strasser, "Smart camera based monitoring system and its application to assisted living," Proceedings of the IEEE, vol. 96, no. 10, pp. 1698-1714, 2008.CrossRefGoogle Scholar
  16. 16.
    J. Krumm, S. Harris, B. Meyers, B. Brumit, M. Hale and S. Shafer, "Multi-camera multi-person tracking for easy living," in IEEE International Workshop on Visual Surveillance, Dublin, 2000.Google Scholar
  17. 17.
    J. Wang and G. Zhang, "Video data mining based on K-Means algorithm for surveillance video," in International Conference on Image Analysis and Signal Processing, Hubei, China, 2011.Google Scholar
  18. 18.
    C. Norris, M. McCahill and D. Wood, "The growth of CCTV: a global perspective on the international diffusion of video surveillance in publicly accessible space," Surveillance and Society, vol. 2, no. 2/3, pp. 110-135, 2004.Google Scholar
  19. 19.
    H. Kruegle, CCTV surveillance: video practices and technology, Elsevier Butterworth-Heinemann, 2007.Google Scholar
  20. 20.
    G. L. Foresti, C. S. Regazzoni and R. Visvanathan, "Scanning the issue/technology: Special issue on video communications, processing and understanding for third generation surveillance systems," Proceedings of the IEEE, vol. 89, no. 10, pp. 1355-1367, October 2001.CrossRefGoogle Scholar
  21. 21.
    C. P. Diehl, "Toward efficient collaborative classification for distributed video surveillance," Pittsburgh, 2000.Google Scholar
  22. 22.
    M. W. Green, "The appropriate and effective use of security technologies in U.S. schools. A guide for schools and law enforcement agencies," 1999.Google Scholar
  23. 23.
    "IP surveillance: the next generation security camera application," July 2005. [Online]. Available:
  24. 24.
    Z. Zhu and T. S. Huang, Multimodal surveillance: sensors, algorithms, and systems, Artech House, 2007.Google Scholar
  25. 25.
    R. T. Collins, A. J. Lipton, T. Kanade, H. Fujiyoshi, D. Duggins, Y. Tsin, D. Tolliver, N. Enomoto and O. Hasegawa, "A system for video surveillance and monitoring," Pittsburgh, 2000.Google Scholar
  26. 26.
    I. Haritaoglu, D. Harwood and L. S. Davis, "W4: real-time surveillance of people and their activities," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, August 2000.CrossRefGoogle Scholar
  27. 27.
    P. Remagnino and G. A. Jones, "Classifying surveillance events from attributes and behaviour," in British Machine Vision Conference, Manchester, 2001.Google Scholar
  28. 28.
    M. Shah, O. Javed and K. Shafique, "Automated visual surveillance in realistic scenarios," IEEE Multimedia, vol. 14, no. 1, pp. 30-39, January 2007.CrossRefGoogle Scholar
  29. 29.
    Y. L. Tian, M. Lu and A. Hampapur, "Robust and efficient foreground analysis for real-time video surveillance," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, 2005.Google Scholar
  30. 30.
    Y. Guan, J. Zhang, Y. Shang, M. Wu and Y. Liu, "Special environment embedded surveillance platform," in China-Japan Joint Microwave Conference, Shanghai, 2008.Google Scholar
  31. 31.
    M. Rahimi, R. Baer, O. I. Iroezi, J. C. Garcia, J. Warrior, D. Estrin and M. Srivastava, "Cyclops: in situ image sensing and interpretation in wireless sensor networks," in International Conference on Embedded Networked Sensor Systems, New York, 2005.Google Scholar
  32. 32.
    F. Dias, P. Chalimbaud, F. Berry, J. Serot and F. Marmoiton, "Embedded early vision systems: implementation proposal and hardware architecture," in Cognitive System for Interactive Sensors, Paris, 2006.Google Scholar
  33. 33.
    I. Downes, L. Baghaei Rad and H. Aghajan, "Development of a mote for wireless image sensor networks," in Cognitive systems for Interactive Sensors, Paris, 2006.Google Scholar
  34. 34.
    Z. Y. Cao, Z. Z. Ji and M. Z. Hu, "An image sensor node for wireless sensor networks," in International Conference on Information Technology: Coding and Computing, Las Vegas, 2005.Google Scholar
  35. 35.
    R. Kleihorst, B. Schueler and A. Danilin, "Architecture and applications of wireless smart cameras (networks)," in IEEE Conference on Acoustics, Speech and Signal Processing, Honolulu, 2007.Google Scholar
  36. 36.
    S. Hengstler, D. Prashanth, S. Fong and H. Aghajan, "MeshEye: a hybrid-resolution smart camera mote for applications in distributed intelligent surveillance," in International Symposium on Information Processing in Sensor Networks, Cambridge, 2007.Google Scholar
  37. 37.
    M. El-Desouki, M. Jamal Deen, Q. Fang, L. Liu, F. Tse and D. Armstrong, "CMOS image sensors for high speed applications," Sensors, Special Issue Image Sensors, vol. 9, no. 1, pp. 430-444, January 2009.Google Scholar
  38. 38.
    K. Lu and et al., "Wireless broadband access: WIMAX and beyond - a secure and service-oriented network control framework for WIMAX networks," IEEE Communication Magazine, no. 45, 2007.Google Scholar
  39. 39.
    L. G. Shapiro and G. G. Stockman, Computer vision, 1 ed., New Jersey: Prentice Hall, 2001.Google Scholar
  40. 40.
    V. Lockton and R. S. Rosenberg, "Technologies of surveillance: evolution and future impact," [Online]. Available:
  41. 41.
    S. Soro and W. Heinzelman, "A survey of visual sensor networks," Advances in Multimedia, vol. 2009, 2009.Google Scholar
  42. 42.
    M. Ghantous, S. Ghosh and M. Bayoumi, "A multi-modal automatic image registration technique based on complex wavelets," in International Conference on Image Processing, Cairo, 2009.Google Scholar
  43. 43.
    M. Ghantous, S. Ghosh and M. Bayoumi, "A gradient-based hybrid image fusion scheme using object extraction," in IEEE International Conference on Image Processing, San Diego, 2008.Google Scholar
  44. 44.
    M. Ghantous and M. Bayoumi, "MIRF: A Multimodal Image Registration and Fusion Module Based on DT-CWT," Springer Journal of Signal Processing Systems, vol. 71, no. 1, pp. 41-55, April 2013.CrossRefGoogle Scholar
  45. 45.
    M. A. Najjar, S. Ghosh and M. Bayoumi, "A hybrid adaptive scheme based on selective Gaussian modeling for real-time object detection," in IEEE Symposium Circuits and Systems, Taipei, 2009.Google Scholar
  46. 46.
    C. Stauffer and W. E. Grimson, "Adaptive background mixture models for real time tracking," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Ft. Collins, 1999.Google Scholar
  47. 47.
    M. A. Najjar, S. Ghosh and M. Bayoumi, "Robust object tracking using correspondence voting for smart surveillance visual sensing nodes," in IEEE International Conference on Image Processing, Cairo, 2009.Google Scholar
  48. 48.
    M. A. Najjar, S. Karlapudi and M. Bayoumi, "A compact single-pass architecture for hysteresis thresholding and component labeling," in IEEE International Conference on Image Processing, Hong Kong, 2010.Google Scholar
  49. 49.
    M. A. Najjar, S. Karlapudi and M. Bayoumi, "Memory-efficient architecture for hysteresis thresholding and object feature extraction," IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3566-3579, December 2011.CrossRefGoogle Scholar
  50. 50.
    M. A. Najjar, S. Karlapudi and M. Bayoumi, "High-performance ASIC architecture for hysteresis thresholding and component feature extraction in limited-resource applications," in IEEE International Conference on Image Processing, Brussels, 2011.Google Scholar
  51. 51.
    J. Canny, "A computational approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, November 1986.CrossRefGoogle Scholar
  52. 52.
    P. Meer and B. Georgescu, "Edge detection with embedded confidence," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 12, pp. 1351-1365, December 2001.CrossRefGoogle Scholar
  53. 53.
    R. Estrada and C. Tomasi, "Manuscript bleed-through removal via hysteresis thresholding," in International Conference on Document Analysis and Recognition, Barcelona, 2009.Google Scholar
  54. 54.
    W. K. Jeong, R. Whitaker and M. Dobin, "Interactive 3D seismic fault detection on the graphics hardware," in International Workshop on Volume Graphics, 2006.Google Scholar
  55. 55.
    C. K. Chui, An Introduction to Wavelets, San Diego: Academic Press, 1992.MATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2014

Authors and Affiliations

  • Mayssaa Al Najjar
    • 1
  • Milad Ghantous
    • 2
  • Magdy Bayoumi
    • 1
  1. 1.University of Louisiana at LafayetteLafayetteUSA
  2. 2.Lebanese International UniversityBeirutLebanon

Personalised recommendations