Abstract
Scene changes such as moved objects, parked vehicles, or opened/closed doors need to be carefully handled so that interesting foreground targets can be detected along with the short-term background layers created by those changes. A simple layered modeling technique is embedded into a codebook-based background subtraction algorithm to update a background model. In addition, important issues related to background updating for visual surveillance are discussed. Experimental results on surveillance examples, such as unloaded packages and unattended objects, are presented by showing those objects as short-term background layers.
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
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Int. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Harville, M.: A framework for high-level feedback to adaptive, per-pixel, mixture-of-gaussian background models. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 543–560. Springer, Heidelberg (2002)
Javed, O., Shafique, K., Shah, M.: A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information. In: IEEE Workshop on Motion and Video Computing, MOTION 2002 (2002)
Porikli, F., Tuzel, O.: Human Body Tracking by Adaptive Background Models and Mean-Shift Analysis. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-ICVS (2003)
Elgammal, A., Harwood, D., Davis, L.S.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000)
Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: Principles and practice of background maintenance. In: Int. Conf. Computer Vision, pp. 255–261 (1999)
Mittal, A., Paragios, N.: Motion-based Background Subtraction Using Adaptive Kernel Density Estimation. In: IEEE Conference in Computer Vision and Pattern Recognition (2004)
Monnet, A., Mittal, A., Paragios, N., Ramesh, V.: Background Modeling and Subtraction of Dynamic Scenes. In: IEEE International Conference on Computer Vision (ICCV), Nice, France (October 2003)
Stenger, B., Ramesh, V., Paragios, N., Coetzee, F., Buhmann, J.M.: Topology free hidden Markov models: application to background modeling. In: IEEE International Conference on Computer Vision, vol. 1, pp. 294–301 (2001)
Mittal, A., Davis, L.S.: M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene Using Region-Based Stereo. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 18–33. Springer, Heidelberg (2002)
Paragios, N., Ramesh, V.: A MRF-based Real-Time Approach for Subway Monitoring. In: IEEE Conference in Computer Vision and Pattern Recognition (2001)
Zhong, J., Sclaroff, S.: Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter. In: IEEE International Conference on Computer Vision (2003)
Garg, K., Nayar, S.K.: Detection and Removal of Rain from Videos. In: IEEE Computer Vision and Pattern Recognition (CVPR), Washington (July 2004)
Zhao, T., Nevatia, R.: Tracking Multiple Humans in Crowded Environment. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2004)
Ren, Y., Chua, C., Ho, Y.: Statistical background modeling for non-stationary camera. Pattern Recognition Letters 24(1-3), 183–196 (2003)
Hayman, E., Eklundh, J.-O.: Statistical Background Subtraction for a Mobile Observer. In: IEEE International Conference on Computer Vision (2003)
Walther, D., Edgington, D.R., Koch, C.: Detection and Tracking of Objects in Underwater Video. In: IEEE International Conference on Computer Vision and Pattern Recognition (2004)
Matsushita, Y., Nishino, K., Ikeuchi, K., Sakauchi, M.: Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1336–1347 (2004)
Davis, J.W., Sharma, V.: Robust Background-Subtraction for Person Detection in Thermal Imagery. In: Joint IEEE Workshop on Object Tracking and Classification Beyond the Visible Spectrum (2004)
Yalcin, H., Black, M.J., Fablet, R.: The Dense Estimation of Motion and Appearance in Layers. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2004)
Ke, Q., Kanade, T.: A Robust Subspace Approach to Layer Extraction. In: IEEE Workshop on Motion and Video Computing (Motion 2002), pp. 37–43 (2002)
Torr, P.H.S., Szeliski, R., Anandan, P.: An Integrated Bayesian Approach to Layer Extraction from Image Sequences. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 297–303 (2001)
Smith, P., Drummond, T., Cipolla, R.: Layered Motion Segmentation and Depth Ordering by Tracking Edges. IEEE Trans. Pattern Anal. Mach. Intell. (April 2004)
Frey, B.J., Jojic, N., Kannan, A.: Learning Appearance and Transparency Manifolds of Occluded Objects in Layers. In: IEEE International Conference on Computer Vision and Pattern Recognition (2003)
Schodl, A., Essa, I.A.: Depth layers from occlusions. In: IEEE International Conference on Computer Vision and Pattern Recognition (2001)
Zhou, Y., Tao, H.: Background Layer Model for Object Tracking through Occlusion. In: IEEE International Conf. on Computer Vision, ICCV 2003, pp. 1079–1085 (2003)
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Background Modeling and Subtraction by Codebook Construction. In: IEEE International Conference on Image Processing, ICIP (2004)
Harwood, D., Subbarao, M., Hakalahti, H., Davis, L.S.: A New Class of Edge-Preserving Smoothing Filters. Pattern Recognition Letters 6, 155–162 (1987)
Haritaoglu, I., Harwood, D., Davis, L.S.: W 4: real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 809–830 (2000)
Cheng, H.-D., Jiang, X.-H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34(12) (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, K., Harwood, D., Davis, L.S. (2005). Background Updating for Visual Surveillance. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds) Advances in Visual Computing. ISVC 2005. Lecture Notes in Computer Science, vol 3804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595755_41
Download citation
DOI: https://doi.org/10.1007/11595755_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30750-1
Online ISBN: 978-3-540-32284-9
eBook Packages: Computer ScienceComputer Science (R0)