Abstract
We describe our approach to segmenting moving objects from the color video data supplied by a nominally stationary camera. There are two main contributions in our work. The first contribution augments Zivkovic and Heijden’s recursively updated Gaussian mixture model approach, with a multi-dimensional Gaussian kernel spatio-temporal smoothing transform. We show that this improves the segmentation performance of the original approach, particularly in adverse imaging conditions, such as when there is camera vibration. Our second contribution is to present a comprehensive comparative evaluation of shadow and highlight detection appoaches, which is an essential component of background subtraction in unconstrained outdoor scenes. A comparative evelaution of these approaches over different color-spaces is currently lacking in the literature. We show that both segmentation and shadow removal performs best when we use RGB color spaces.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zivkovic, Z., Heijden, F., van der Heijden, F.: Recursive unsupervised learning of finite mixture models. IEEE Transaction on Pattern Analysis and Machine Intelligence 26(5), 651–656 (2004)
Friedman, N., Russell, S.: Image segmentation in video sequences: a probabilistic approach. In: Proc 13th Conf. on Uncertainty in Artificial Intelligence, pp. 175-181 (1997)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Power, P.W., Schoonees, J.A.: Understanding background mixture models for foreground segmentation. In: Proceedings of Image and Vision Computing, New Zealand (2002)
Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)
KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Proc. of 2nd European workshop on Advanced Video Based Surveillance Systems, ch. 11, pp. 135–144 (2001)
Lee, D.-S.: Effective Gaussian mixture learning for video background subtraction. IEEE Transaction on Pattern Analysis and Machine Intelligence 27(5), 827–832 (2005)
Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting moving shadows: algorithms and evaluation. IEEE Transaction on Pattern Analysis and Machine Intelligence 25(7), 918–923 (2003)
Cucchiara, R., Piccardi, M., Prati, A.: Detecting moving objects, ghosts and shadows in video streams. IEEE Transaction on Pattern Analysis and Machine Intelligence 25(10), 1337–1342 (2003)
Zivkovic, Z., Heijden, F., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters 27(7), 773–780 (2006)
Chen, Z., Husz, Z.L., Wallace, I., Wallace, A.M.: Video object tracking based on a Chamfer distance transform. In: IEEE International Conference on Image Processing, San Antonio, Texas, USA, pp. 357–360 (2007)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(5), 603-619 (2002)
Joachims, T.: Training linear SVMs in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), Philadelphia, Pennsylvania, USA, pp. 217–226 (2006)
Elgammal, A.M., Harwood, A., Davis, L.S.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000)
Finlayson, G., Hordley, S., Drew, M.: Removing shadows from images. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 823–836. Springer, Heidelberg (2002)
Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: Proceedings of IEEE ICCV 1999 Frame rate workshop (1999)
Elgammal, A., Duraiswami, R., Harwood, A., Davis, L.S.: Background and foreground modelling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE 90(7), 1151–1163 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Z., Pears, N., Freeman, M., Austin, J. (2009). Background Subtraction in Video Using Recursive Mixture Models, Spatio-Temporal Filtering and Shadow Removal. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_109
Download citation
DOI: https://doi.org/10.1007/978-3-642-10520-3_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10519-7
Online ISBN: 978-3-642-10520-3
eBook Packages: Computer ScienceComputer Science (R0)