Abstract
In this paper we address the context of visual surveillance in outdoor environments involving the detection of moving objects in the observed scene. In particular, a reliable foreground segmentation, based on a background subtraction approach, is explored. We firstly address the problem arising when small movements of background objects, as trees blowing in the wind, generate false alarms. We propose a background model that uses a supervised training for coping with these situations. In addition, in real outdoor scenes the continuous variations of lighting conditions determine unexpected intensity variations in the background model parameters. We propose a background updating algorithm that work on all the pixels in the background image, even if covered by a foreground object. The experiments have been performed on real image sequences acquired in a real archeological site.
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References
S. Fejes, L.S. Davis, Detection of independent motion using directional motion estimation, Technical Report. CAR-TR-866, CS-TR 3815, Univ. of Mar., Aug. 1997.
S. Fejes, L.S. Davis, What can projections of flow fields tell us about the visual motion, In Proc Intern. Confer. on Computer Vision ICCV98, 1998, pp. 979–986.
S. Fejes, L.S. Davis, Exploring visual motion using projections of flow fields, In. Proc. of the DARPA Image Underst. Work., pp.113–122, New Orleans, LA,1997.
L. Wixson, M. Hansen, Detecting salient motion by accumulating directionalconsistent flow, In proc. of Intern. Conf. on Comp. Vis., 1999, vol II, pp 797–804.
C. Anderson, P. Burt, G. Van Der Wal, Change detection and tracking using pyramid transformation techniques, In Proc. of SPIE-Intell. Robots and Comp. Vision Vol. 579, pp.72–78, 1985.
I. Haritaoglu, D. Harwood, L. Davis, A Fast Background Scene Modeling and Maintenance for Outdoor Surveillance, ICPR, pp.179–183, Barcelona,2000.
C. Wren, A. Azarbayejani, T. Darrell, A. Pentland, Pfinder: Real-time tracking of the human body, IEEE Trans. on Patt. An. and Mach. Intell. 19(7): pp.780–785, 1997.
T. Kanade, T. Collins, A. Lipton, Advances in Cooperative Multi-Sensor Video Surveillance, Darpa Image Underst. Work., Morgan Kaufmann, Nov. 1998, pp. 3–24.
H. Fujiyoshi, A. J. Lipton, Real-time human motion analysis by image skeletonisation, IEEE WACV, Princeton NJ, October 1998, pp.15–21.
P. Spagnolo, A. Branca, G. Attolico, A. Distante: Fast Background Modeling and Shadow Removing for Outdoor Surveillance, IASTED VIIP, 2002, pag. 668–671.
M. Leo, G. Attolico, A. Branca, A. Distante: Object classification with multiresolution wavelet decomposition, in Proc. of SPIE Aerosense 2002, conference on Wavelet Applications, 1–5 April, 2002, Orlando, Florida, USA.
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Spagnolo, P., Leo, M., Attolico, G., Distante, A. (2003). A Supervised Approach in Background Modelling for Visual Surveillance. In: Kittler, J., Nixon, M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44887-X_70
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DOI: https://doi.org/10.1007/3-540-44887-X_70
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