A Novel Technology in Surveillance Video for Detecting and Forecasting a Robbing Incident

Abstract:

In this paper, a novel technology, which detects and forecasts human's abnormal behavior, is proposed. Taking an abnormal behavior, the robbery, as an example, the accuracy of the technology is demonstrated successfully. There are 3 innovations in this paper. A) A robbery incident is divided into 5 phases occurred orderly. By its order, a robbery is detected. B) By human body's motion character, the human body's speeds are measured in different areas and at different times. It improves the accuracy rate of a classifier. C) The technology can forecast an abnormal human behavior in a short time advance. Experiment shows that the technology detecting and forecasting accuracy are high enough to be used in practice.

• Periodical: Applied Mechanics and Materials (Volumes 241-244)
• Pages: 1647-1651
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.241-244.1647
• Online since: December 2012
• Authors: Da Peng Gao, Qing Xing Zhu
• Keywords: Detecting and Forecasting, Robbing Incident, Surveillance Video

References

[1] N. Paragios and R. Deriche 2000. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans. Pattern Anal. Machine Intell. vol. 22. 266–280.

DOI: 10.1109/34.841758

[2] A. Elgammal, R. Duraiswami, D. Harwood, and L.S. Davis 2002. Background and foreground modeling using nonparametric kernel density for visual surveillance. Proc. IEEE. 90. 1151–1163.

DOI: 10.1109/jproc.2002.801448

[3] P. -M. Jodoin, V. Saligrama, and J. Konrad Jan. 2008. Behavior subtraction. Proc. SPIE Visual Communications and Image Process. 6822. 10. 1–10. 12.

DOI: 10.1117/12.770757

[4] T. Sziranyi Z. Szlavik, L. Havasi 2007. Geometrical scene analysis using co-motion statistics. LECTURE NOTES IN COMPUTER SCIENCE. 4678. 968–979.

DOI: 10.1007/978-3-540-74607-2_88

[5] W. Hu, T. Tab, L. Wang, and S. Maybank 2004. A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern˙ - Part C: App. And Reviews. 34. 334–352.

DOI: 10.1109/tsmcc.2004.829274

[6] Ermis.E. B, Saligrama. V, Jodoin.P. -M, Konrad. J, Boston. Univ, Boston. MA Sep 2008 Abnormal behavior detection and behavior matching for networked cameras. Distributed Smart Cameras 2008. ICDSC 2008. Second ACM/IEEE International Conference, Stanford, CA, , 1 - 10.

DOI: 10.1109/icdsc.2008.4635728

[7] OpenCV. http: /www. sourceforge. net/projects/opencvlibrary, (2010).

[8] CASIA Action Database. http: /www. sinobiometrics. com, 2007.

[9] Lipton A，Fujiyoshi H，Patil R 1998. Moving target classification and tracking from real-time video[C]．IEEE Workshop on Applications of Computer Vision, Princeton：IEEE Press，： 8-14.

DOI: 10.1109/acv.1998.732851