Abstract
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, which is one of the most crucial aspects to be taken into consideration during the development of visual surveillance system. The mathematical principles related to background model creation and object classification are included. We incorporated several improvements to the background subtraction method for shadow removal, lighting change adaptation and integration of fragmented foreground regions. The type of the static regions is determined by using a method that exploits context information about foreground masks, significantly outperforming previous edge-based techniques. Developed algorithm has been implemented as a working model involving freely available OpenCV library and tested on benchmark data taken from real visual surveillance systems.
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References
Thirde, D., Li, L., Ferryman, J.: Overview of the pets2006 challenge. In: Ninth IEEE International Workshop on Performance Evaulation of Tracking and Surveillance, PETS 2006 (2006)
Ferryman, J.M. (ed.): Proceedings Tenth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2007), Rio de Janeiro, Brazil (October 2007)
Regazzoni, C.S., Fabri, G., Vernazza, G.: Advanced Video-Based Surveillance Systems. Springer, Heidelberg (1999)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 1999), vol. 2 (1999)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–577 (2003)
Welch, G., Bishop, G.: An introduction to the kalman flter, course 8. In: SIGGRAPH (2001)
Li, L., Ma, R., Huang, W., Leman, K.: Evaluation of an ivs system for abandoned object detection on pets 2006 datasets. In: Ninth IEEE International Workshop on Performance Evaulation of Tracking and Surveillance, PETS 2006 (2006)
del Rincon, J.M., Elias Herrero-Jaraba, J., Gomez, J.R., Orrite-Urunuela, C.: Automatic left luggage detecion and tracking using multi camera ukf. In: Ninth IEEE International Workshop on Performance Evaulation of Tracking and Surveillance, PETS 2006 (2006)
Tian, Y., Feris, R.S., Hampapur, A.: Real-time detection of abandoned and removed objects in complex environments. In: IEEE International Workshop on Visual Surveillance (in Conjunction with ECCV 2008), Marseille, France (2008)
Cucchiara, R., Grana, C., Piccardi, M., Prati, A., Sirotti, S.: Improving shadow suppression in moving object detection with hsvcolor information. IEEE Intelligent Transportation Systems, 334–339 (2001)
Piovoso, M., Laplante, P.A.: Kalman filter recipes for real-time image processing. Real-Time Imaging 9(6), 433–439 (2003)
Fengjun, L., Xuefeng, S., Bo, W., Singh Vivek, K., Ramakant, N.: Left-luggage detection using bayesian inference. In: Ninth IEEE International Workshop on Performance Evaulation of Tracking and Surveillance (PETS 2006), pp. 83–90 (2006)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. INRIA, IEEE Computer Society, Washington (2005)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)
Open Computer vision library (2009), http://sourceforge.net/projects/opencvlibrary/
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Forczmański, P., Seweryn, M. (2010). Surveillance Video Stream Analysis Using Adaptive Background Model and Object Recognition. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2010. Lecture Notes in Computer Science, vol 6374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15910-7_13
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DOI: https://doi.org/10.1007/978-3-642-15910-7_13
Publisher Name: Springer, Berlin, Heidelberg
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