SmartMonitor: recent progress in the development of an innovative visual surveillance system

• Autorzy: Frejlichowski D. , Gościewska K. , Forczmański P. , Nowosielski A. , Hofman R.
• Języki publikacji: EN

Abstrakty

EN

This paper describes recent improvements in developing SmartMonitor — an innovative security system based on existing traditional surveillance systems and video content analysis algorithms. The system is being developed to ensure the safety of people and assets within small areas. It is intended to work without the need for user supervision and to be widely customizable to meet an individual’s requirements. In this paper, the fundamental characteristics of the system are presented including a simplified representation of its modules. Methods and algorithms that have been investigated so far alongside those that could be employed in the future are described. In order to show the effectiveness of the methods and algorithms described, some experimental results are provided together with a concise explanation.

Słowa kluczowe

EN

SmartMonitor; visual surveillance system; video content analysis

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2012
• Tom: Vol. 6, nr 3
• Strony: 28--35
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Frejlichowski D.

autor

Gościewska K.

autor

Forczmański P.

autor

Nowosielski A.

autor

Hofman R.

• Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland,

Bibliografia

• [1] Bosch IVA 4:0 Commercial Brochure, http:// resource.boschsecurity.com/documents/Commercial Brochure enUS 1558886539.pdf
• [2] Robertson N., Reid I.: A general method for human activity recognition in video. Computer Vision and Image Understanding 104, 232–248 (2006)
• [3] Gurwicz Y., Yehezkel R., Lachover B.: Multiclass object classification for real-time video surveillance systems. Pattern Recognition Letters 32, 805–815 (2011)
• [4] Frejlichowski D., Forczmański P., Nowosielski A., Gościewska K., Hofman R.: SmartMonitor: An Approach to Simple, Intelligent and Affordable Visual Surveillance System. In: Bolc, L. et al.(eds.) ICCVG 2012. LNCS, vol. 7594, pp. 726–734. Springer, Heidelberg (2012) SmartMonitor: recent progress. 35
• [5] Forczma´nski P., Frejlichowski D., Nowosielski A., Hofman R.: Current trends in the development of intelligent visual monitoring systems (in Polish). Methods of Applied Computer Science 4/2011(29), 19–32 (2011)
• [6] Frejlichowski D.: Automatic Localisation of Moving Vehicles in Image Sequences Using Morphological Operations. 1st IEEE International Conference on Information Technology, 439-442 (2008)
• [7] Stauffer C., GrimsonW. E. L.: Adaptive background mixture models for real-time tracking. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2–252 (1999)
• [8] Zivkovic Z.: Improved adaptive Gaussian mixture model for background subtraction. Proceedingsof the 17th International Conference on Pattern Recognition 2, 28–31 (2004)
• [9] Forczma´nski P., Seweryn M.: Surveillance Video Stream Analysis Using Adaptive Background Model and Object Recognition. In: Bolc, L. et al. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 114–121. Springer, Heidelberg (2010)
• [10] Welch G., Bishop G.: An Introduction to the Kalman Filter. UNC-Chapel Hill, TR 95-041 (24 July 2006)
• [11] Cheng Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(8), 790–799 (1995)
• [12] Comaniciu D., Meer P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)
• [13] Viola P., Jones M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1, 511–518 (2001)
• [14] Avidan S.: Ensemble Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(2), 261–271 (2007)
• [15] Dalal N., Triggs B.: Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1, 886–893 (2005)