Abstract
In many visual tracking and surveillance systems, it is important to initialize a background model using a training video sequence which may include foreground objects. In such a case, robust statistical methods are required to handle random occurrences of foreground objects (i.e., outliers), as well as general image noise. The robust statistical method Median has been employed for initializing the background model. However, the Median can tolerate up to only 50% outliers, which cannot satisfy the requirements of some complicated environments. In this paper, we propose a novel robust method for the background initialization. The proposed method can tolerate more than 50% of foreground pixels and noise. We give quantitative evaluations on a number of video sequences and compare our proposed method with five other methods. Experiments show that our method can achieve very promising results in background initialization: including applications in video segmentation, visual tracking and surveillance.
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
Gloyer, B., et al.: Video-based Freeway Monitoring System Using Recursive Vehicle Tracking. In: Proc. of IS&T-SPIE Symposium on Electronic Imaging: Image and Video Processing, pp. 173–180 (1995)
Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-Time Surveillance of People and Their Activities. IEEE Trans. Pattern Analysis and Machine Intelligence 22(8), 809–830 (2000)
Wren, C.R., et al.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 780–785 (1997)
Cristani, M., Bicego, M., Murino, V.: Multi-level background initialization using Hidden Markov Models. In: First ACM SIGMM international workshop on Video surveillance, pp. 11–20 (2003)
Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-time Tracking. In: Proc. IEEE Int’l Conf. on Computer Vision and Pattern Recognition, pp. 246–252 (1999)
Toyama, K., et al.: Wallflower: Principles and Practice of Background Maintenance. In: 7th International Conference on Computer Vision, pp. 255–261 (1999)
Harville, M.: A Framework for High-Level Feedback to Adaptive, Per-Pixel, Mixture-of-Gaussian Background Models. In: 7th European Conference on Computer Vision, pp. 543–560 (2002)
Friedman, N., Russell, S.: Image Segmentation in Video Sequences: A Probabilistic Approach. In: Proc. Thirteenth Conf. on Uncertainty in Artificial Intelligence, pp. 175–181 (1997)
Harville, M., Gordon, G., Woodfill, J.: Foreground Segmentation Using Adaptive Mixture Models in Color and Depth. In: IEEE Workshop on Detection and Recognition of Events in Video, pp. 3–11 (2001)
Gutchess, D., et al.: A Background Model Initialization Algorithm for Video Surveillance. In: IEEE Int’l Conference on Computer Vision, pp. 733–740 (2001)
Long, W., Yang, Y.H.: Stationary Background Generation: An Alternative to the Difference of Two Images. Pattern Recognition 23(12), 1351–1359 (1990)
Stewart, C.V.: Robust Parameter Estimation in Computer Vision. SIAM Review 41(3), 513–537 (1999)
Fischler, M.A., Rolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Commun. ACM 24(6), 381–395 (1981)
Wang, H., Suter, D.: Robust Adaptive-Scale Parametric Model Estimation for Computer Vision. IEEE Trans. Pattern Analysis and Machine Intelligence 26(11), 1459–1474 (2004)
Wang, H., Suter, D.: Background Initialization with A New Robust Statistical Approach. In: IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 153–159 (2005)
Horprasert, T., Harwood, D., Davis, L.S.: A Statistical Approach for Real-Time Robust Background Subtraction and Shadow Detection. In: ICCV 1999 Frame-Rate Workshop (1999)
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
Wang, H., Suter, D. (2006). A Novel Robust Statistical Method for Background Initialization and Visual Surveillance. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_34
Download citation
DOI: https://doi.org/10.1007/11612032_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31219-2
Online ISBN: 978-3-540-32433-1
eBook Packages: Computer ScienceComputer Science (R0)