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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
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)
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)
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)
DARPA program: Combat Zones That See, (2003)
Cooperative Distributed Vision. http://vision.kuee.kyotou.ac.jp/CDVPRJ/
Qian, R., Haering, N., Sezan, I.: A Computational Approach to Semantic Event Detection. CVPR , pp. 1200–1206 (1999)
Haering, N., da V. Lobo, N.: Visual Event Detection, Kluwer, (2001)
Isard M. and Blake A. (1998). CONDENSATION – conditional density propagation for visual tracking. Int. J. Computer Vision. 29(1): 5–28
Isard, M., MacCormick, J.: BraMBLe: A Bayesian multiple-blob tracker, Proc. ICCV, (2001)
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)
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
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)
Research And Markets, Report on Closed Circuit TV Industry —A Market Update (2005–2008), 2005
Zhong, H., Shi, J. Visontai, M.: Detecting Unusual Activity in Video, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), (2004)
Stauffer, C., Grimson, W.: Adaptive Background Mixture Models for Real-Time Tracking, IEEE Conference on Computer Vision and Pattern Recognition, (1999)
Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and Practice of Background Maintenance, International Conference on Computer Vision, pp. 255–261, (1999)
Video Surveillance And Monitoring, part of DARPA’s Image Understanding for Battlefield Awareness (IUBA) program, (1996)
About this article
Cite this article
Haering, N., Venetianer, P.L. & Lipton, A. The evolution of video surveillance: an overview. Machine Vision and Applications 19, 279–290 (2008). https://doi.org/10.1007/s00138-008-0152-0
- Object recognition
- Object-based video segmentation
- Video surveillance
- Visual tracking
- Surveillance system
- Scene segmentation
- Target detection
- Vision system