Two Examples of Indoor and Outdoor Surveillance Systems: Motivation, Design, and Testing
We examine the state of the security industry and market and underline the role that it plays in the R&D efforts. We also present a snapshot of the current state-of-the-art in indoor and outdoor surveillance systems for commercial applications. Then, we move on and describe in detail a prototype indoor surveillance system that we recently developed. The system is called Cooperative Camera Network (CCN) and reports the presence of a visually tagged individual throughout a building structure. Visual tagging is based on the color signature of a person. CCN is meant to be used for the monitoring of potential shoplifters in department stores. We also describe our prototype outdoor surveillance system, the DETER (Detection of Events for Threat Evaluation and Recognition). DETER can monitor large open spaces, like parking lots, and report unusual moving patterns by pedestrians or vehicles. To perform its function DETER fuses the field of views of multiple cameras into a sup er-view and performs tracking of moving objects across it. A threat assessment module with encoded suspicious behaviors performs the motion pattern identification. Both surveillance systems are good examples of technology transfer of state-of-the-art ideas from the research literature to the commercial domain. At the same time, they are good study cases for the extra engineering methodology and effort that is needed to adapt initial research concepts into a successful practical technology.
KeywordsSurveillance security systems threat assessment color recognition multi-normal representation
Unable to display preview. Download preview PDF.
- World security services to 2004. Technical Report 1348, The Preedonia Group, December 2000.Google Scholar
- Q. Cai and J.K. Aggarwal. Automatic tracking of human motion in indoor scenes across multiple synchonized video streams. In Proceedings Sixth International Conference on Computer Vision, pages 356–362, Bombay, India, January 4–7 1999.Google Scholar
- Q. Cai, A. Mitiche, and J.K. Aggarwal. Tracking human motion in an indoor environment. In Proceedings 1995 IEEE International Conference on Image Processing, volume 1, pages 215–218, Washington D.C., October 23–26 1995.Google Scholar
- W.E.L. Grimson, C. Stauffer, R. Romano, and L. Lee. Using adaptive tracking to classify and monitor activities in a site. In Proceedings 1998 IEEE Conference on Computer Vision and Pattern Recognition, pages 22–29, Santa Barbara, CA, June 23–25 1998.Google Scholar
- T. Kanade, R.T. Collins, A.J. Lipton, P. Burt, and L. Wixson. Advances in cooperative multi-sensor video surveillance. In Proceedings DARPA Image Understanding Workshop, pages 3–24, Monterey, CA, November 1998.Google Scholar
- N. Otsu. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(l):62–66, 1979.Google Scholar
- C. Stauffer and W.E.L. Grimson. Adaptive background mixture models for real-time tracking. In Proceedings 1999 IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 246–252, Fort Collins, CO, June 23–25 1999.Google Scholar
- F. Ziliani and A. Cavallaro. Image analysis for video surveillance based on spatial regularization of a statistical model-based change detection. In Proceedings 1999 International Conference on Image Analysis and Processing, pages 1108–1111, Venice, Italy, September 27–29 1999.CrossRefGoogle Scholar