Object classification in surveillance stream using adaptive background model

• Autorzy: Forczmański P. , Seweryn M.
• Języki publikacji: EN

Abstrakty

EN

One of the most crucial aspects to be taken into consideration during the development of visual surveillance system is the need to inform the human operator about any unusual situation happening. The paper presents an idea of real-time video stream analysis which leads to the detection and tracking of suspicious objects that have been left unattended. The mathematical principles related to background model creation and detection tuning are included. Developed algorithm has been implemented as a working model involving OpenCV library and tested on benchmark data taken from real visual surveillance system.

Słowa kluczowe

EN

visual surveillance; background model; object detection; object tracking

PL

monitoring wizyjny; tło modelu; wykrywanie objektów; śledzenie objektu

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Metody Informatyki Stosowanej
• Rocznik: 2009
• Tom: nr 4 (21)
• Strony: 17--22
• Opis fizyczny: Bibliogr. 10, rys., tab.

Twórcy

autor

Forczmański P.

autor

Seweryn M.

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

Bibliografia

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• [2] J. M. Ferryman (Editor). Proceedings Tenth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, (PETS 2007), 2007.
• [3] C. S. Regazzoni, G. Fabri, G. Vernazza. Advanced Video-Based Surveillance Systems. Springer, 1999.
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• [5] R. Cucchiara, C. Grana, M. Piccardi, A. Prati, S. Sirotti. Improving shadow suppression in moving object detection with HSVcolor information. IEEE Intelligent Transportation Systems, pp. 334–339, 2001.
• [6] M. Piovoso, P. A. Laplante. Kalman filter recipes for real-time image processing. Real-Time Imaging, 9:433–439, December 2003.
• [7] F. Lv, X. Song, B. Wu, V. K. Singh, R. Necatia. Left-Luggage Detection using Bayesian Inference. Ninth IEEE International Workshop on Performance Evaulation of Tracking and Surveillance (PETS 2006), pp. 83–90. Institute for Robotics and Intelligent Systems, University of Southern California, 2006.
• [8] N. Dalal, B. Triggs. Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893, 2005.
• [9] P. Viola, M. Jones. Rapid Object Detection Using a Boosted Cascade of Simple Features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 511–518, 2001.
• [10] E. Auvinet, E. Grossmann, C. Rougier, M. Dahmane, J. Meunier. Left-luggage detection using homographies and simple heuristics. Proc. IEEE Workshop on Performance Evaluation in Surveillance and Tracking, 2006.