Abstract
A common problem that one could encounter in motion estimation of indoor, or yet more, of daytime outdoor scenes is that of the detection of shadows attached to their respective moving objects. The detection of a shadow as a legitimate moving region may mislead an algorithm for the subsequent phases of analysis and tracking, which is why moving objects should be separated from their shadow. This paper presents work we have done to detect moving shadows in gray level scenes in real time for visual surveillance purposes. In this work we do not rely on any a priori information regarding with color, shape or motion speed to detect shadows. Rather, we exploit some statistical properties of the shadow borders after they have been enhanced through a simple edge gradient based operation. We developed the overall algorithm using a challenging outdoor traffic scene as a “training” sequence. Secondly, we assess the effectiveness of our shadow detection method by extracting the ground truth from gray level sequences taken indoors and outdoors from different urban and highway traffic scenes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bevilacqua, A., Di Stefano, L., Lanza, A.: An efficient motion detection algorithm based on a statistical non parametric noise model. In: 17th IEEE International Conference on Image Processing (ICIP 2004), Singapore, October 2004, pp. 2347–2350 (2004)
Friedman, N., Russell, S.: Image segmentation in video sequences: A probabilistic approach. In: 30th Conference on Uncertainty in Artificial Intelligence, Providence, RI, USA (1997)
Mikić, I., Cosman, P.C., Kogut, G.T., Trivedi, M.M.: Moving Shadows and Object Detection in Traffic Scenes. In: Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, vol. 1, pp. 321–324 (2003)
Rosin, P., Ellis, T.: Image difference threshold strategies and shadow detection. In: 6th British Machine Vision Conference, Birmingham, UK, pp. 347–356 (1995)
Stauder, J., Mech, R., Ostermann, J.: Detection of moving cast shadows for object segmentation. IEEE Transactions on Multimedia 1(1), 65–76 (1999)
Prati, A., Mikić, I., Trivedi, M.M., Cucchiara, R.: Detecting Moving Shadows: Algorithms and Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence X(X), XX–XX (2003)
Yi-Ming, W., Xiu-Qing, Y., Wei-Kang, G.: A shadow handler in traffic monitoring system. In: IEEE 55th Vehicular Technology Conference (VTC) Spring 2002, vol. 1, pp. 303–307 (2002)
Bevilacqua, A.: Effective shadow detection in traffic monitoring applications. Journal of WSCG 11(1), 57–64 (2003)
Bevilacqua, A.: Effective object segmentation in a traffic monitoring application. In: 3rd IAPR ICVGIP Conference, Ahmedabad, India, pp. 125–130 (2002)
Cervenka, V., Charvat, K.: Survey of the image processing research applicable to the thematic mapping based on aerocosmic data. Technical report, Geodetic and Carthographic Institute, Prague, Czechoslovakia (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bevilacqua, A. (2006). A Novel Shadow Detection Algorithm for Real Time Visual Surveillance Applications. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_82
Download citation
DOI: https://doi.org/10.1007/11867661_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44894-5
Online ISBN: 978-3-540-44896-9
eBook Packages: Computer ScienceComputer Science (R0)