Abstract
Visual surveillance systems include a wide range of related areas ranging from motion detection, moving object classification and tracking to activity understanding. Typical applications include traffic surveillance, CCTV security systems, road sign detection. Each of the above-mentioned applications relies greatly on proper motion segmentation method. Many background subtraction algorithms have been proposed. Simple yet robust frame differencing, statistically based Mixture of Gaussians or sophisticated methods based on wavelets or the optical flow computed by the finite element method. In this paper we focus on novel modification of well known MoG. The intrinsic motivation stems from the inability of regular MoG implementation to handle many camera related phenomena. Here presented method exploits Histograms of Oriented Gradients to significantly reduce the influence of camera jitter, automatic iris adjustment or exposure control causing severe degradation of foreground mask. The robustness of introduced method is shown on series of video sequences exhibiting mentioned phenomena.
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.
Similar content being viewed by others
References
Cheung, S.C.S., Kamath, C.: Robust techniques for background subtraction in urban traffic. In: Chen, L., Ryan, M.D., Wang, G. (eds.) ICICS 2008. LNCS, vol. 5308, pp. 881–892. Springer, Heidelberg (2008)
Buch, N., Yin, F., Orwell, J., Makris, D., Velastin, S.A.: Urban vehicle tracking using a combined 3d model detector and classifier. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds.) Knowledge-Based and Intelligent Information and Engineering Systems. LNCS, vol. 5711, pp. 169–176. Springer, Heidelberg (2009)
Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 780–785 (1997)
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S., Duraiswami, R., Harwood, D.: Background and foreground modeling using nonparametric kernel density for visual surveillance. Proceedings of the IEEE, 1151–1163 (2002)
Pisheh, M.A.Z., Sheikhi, A.: Detection and compensation of image sequence jitter due to an unstable ccd camera for video tracking of a moving target. In: 3DPVT 2004: Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium, Washington, DC, USA, pp. 258–261. IEEE Computer Society, Los Alamitos (2004)
Im, T.H., Eom, I.K., Kim, Y.S.: Wavelet-based moving object segmentation using background registration technique. In: SIP 2007: Proceedings of the Ninth IASTED International Conference on Signal and Image Processing, Anaheim, CA, USA, pp. 84–88. ACTA Press (2007)
Antic, B., Castaneda, J., Culibrk, D., Pizurica, A., Crnojevic, V., Philips, W.: Robust detection and tracking of moving objects in traffic video surveillance. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 494–505. Springer, Heidelberg (2009)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Schmid, C., Soatto, S., Tomasi, C. (eds.) International Conference on Computer Vision & Pattern Recognition. INRIA Rhône-Alpes, ZIRST-655, av. de l’Europe, Montbonnot-38334, vol. 2, pp. 886–893 (2005)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: IJCAI 1981: Proceedings of the 7th International Joint Conference on Artificial Intelligence, pp. 674–679. Morgan Kaufmann Publishers Inc., San Francisco (1981)
Power, P.W., Schoonees, J.A.: Understanding background mixture models for foreground segmentation. In: Proceedings of the Image and Vision Computing, pp. 267–271 (2002)
Bilmes, J.: A gentle tutorial of the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical report, International Computer Science Institute (1998)
Stefano, L.D., Neri, G., Viarani, E.: Analysis of pixel-level algorithms for video surveillance applications. In: 11th International Conference on Image Analysis and Processing, ICIAP 2001, pp. 542–546 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fabian, T. (2010). Mixture of Gaussians Exploiting Histograms of Oriented Gradients for Background Subtraction. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17274-8_70
Download citation
DOI: https://doi.org/10.1007/978-3-642-17274-8_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17273-1
Online ISBN: 978-3-642-17274-8
eBook Packages: Computer ScienceComputer Science (R0)