Machine Vision and Applications

, Volume 19, Issue 5–6, pp 279–290 | Cite as

The evolution of video surveillance: an overview

  • Niels Haering
  • Péter L. Venetianer
  • Alan Lipton
Special Issue Paper


Over the past 10 years, computer vision research has matured significantly. Although some of the core problems, such as object recognition and shape estimation are far from solved, many applications have made considerable progress. Video Surveillance is a thriving example of such an application. On the one hand, worldwide the number of cameras is expected to continue to grow exponentially and security budgets for governments, corporations and the private sector are increasing accordingly. On the other hand, technological advances in target detection, tracking, classification, and behavior analysis improve accuracy and reliability. Simple video surveillance systems that connect cameras via wireless video servers to Home PCs offer simple motion detection capabilities and are on sale at hardware and consumer electronics stores for under $300. The impact of these advances in video surveillance is pervasive. Progress is reported in technical and security publications, abilities are hyped and exaggerated by industry and media, benefits are glamorized and dangers dramatized in movies and politics. This exposure, in turn, enables the expansion of the vocabulary of video surveillance systems paving the way for more general automated video analysis.


Object recognition Object-based video segmentation Video surveillance Visual tracking Surveillance system Scene segmentation Target detection Vision system 


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  1. 1.
    Boult, T.E., Micheals, R., Gao, X., Lewis, P., Power, C., Yin, W., Erkan, A.: Frame-Rate Omnidirectional Surveillance and Tracking of Camouflaged and Occluded Targets, Proc. Workshop on Visual Surveillance, Fort Collins, CO, June (1999)Google Scholar
  2. 2.
    Cohen, I., Medioni, G.: Detecting and tracking moving objects for video surveillance, in Proc. IEEE Computer Vision and Pattern Recognition, Fort Collins (CO), USA, June (1999)Google Scholar
  3. 3.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-Time Tracking of Non-Rigid Objects using Mean Shift, IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, pp. 142–149, (2000)Google Scholar
  4. 4.
    DARPA program: Combat Zones That See, (2003)Google Scholar
  5. 5.
    Cooperative Distributed Vision.
  6. 6.
    Qian, R., Haering, N., Sezan, I.: A Computational Approach to Semantic Event Detection. CVPR , pp. 1200–1206 (1999)Google Scholar
  7. 7.
    Haering, N., da V. Lobo, N.: Visual Event Detection, Kluwer, (2001)Google Scholar
  8. 8.
    Isard M. and Blake A. (1998). CONDENSATION – conditional density propagation for visual tracking. Int. J. Computer Vision. 29(1): 5–28 CrossRefGoogle Scholar
  9. 9.
    Isard, M., MacCormick, J.: BraMBLe: A Bayesian multiple-blob tracker, Proc. ICCV, (2001)Google Scholar
  10. 10.
    Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems, Transactions of the ASME–Journal of Basic Engineering. 82, Series D, pp. 35–45 (1960)Google Scholar
  11. 11.
    Lipton A., Heartwell C., Haering N. and Madden D. (2003). Automated Video Protection, Monitoring & Detection. IEEE Aerospace and Electronic Systems Magazine. 18(5): 3–18 CrossRefGoogle Scholar
  12. 12.
    Lipton, A.: Intelligent Video as a Force Multiplier for Crime Detection and Prevention. IEE International Symposium on Imaging for Crime Detection and Prevention. pp. 151–156. London, (2005)Google Scholar
  13. 13.
    Research And Markets, Report on Closed Circuit TV Industry —A Market Update (2005–2008), 2005Google Scholar
  14. 14.
    Zhong, H., Shi, J. Visontai, M.: Detecting Unusual Activity in Video, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), (2004)Google Scholar
  15. 15.
    Stauffer, C., Grimson, W.: Adaptive Background Mixture Models for Real-Time Tracking, IEEE Conference on Computer Vision and Pattern Recognition, (1999)Google Scholar
  16. 16.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and Practice of Background Maintenance, International Conference on Computer Vision, pp. 255–261, (1999)Google Scholar
  17. 17.
    Video Surveillance And Monitoring, part of DARPA’s Image Understanding for Battlefield Awareness (IUBA) program, (1996)Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Niels Haering
    • 1
  • Péter L. Venetianer
    • 1
  • Alan Lipton
    • 1
  1. 1.ObjectVideoRestonUSA

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