This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Bayesian Modeling of Dynamic Scenes for Object Detection
November 2005 (vol. 27 no. 11)
pp. 1778-1792
ASCII Text
x 
 
Yaser Sheikh, Mubarak Shah, "Bayesian Modeling of Dynamic Scenes for Object Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 11, pp. 1778-1792, November, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.213,
author = {Yaser Sheikh and Mubarak Shah},
title = {Bayesian Modeling of Dynamic Scenes for Object Detection},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {11},
issn = {0162-8828},
year = {2005},
pages = {1778-1792},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.213},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Bayesian Modeling of Dynamic Scenes for Object Detection
IS - 11
SN - 0162-8828
SP1778
EP1792
EPD - 1778-1792
A1 - Yaser Sheikh,
A1 - Mubarak Shah,
PY - 2005
KW - Index Terms- Object detection
KW - kernel density estimation
KW - joint domain range
KW - MAP-MRF estimation.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes.

[1] J. Besag, “On the Statistical Analysis of Dirty Pictures,” J. Royal Statistical Soc., vol. 48, 1986.
[2] Y. Boykov, O. Veksler, and R. Zabih, “Fast Approximate Energy Minimization Via Graph Cuts,” IEEE Trans. Pattern Analysis and Machine Intelligence, 2001.
[3] R. Collins and Y. Liu, “On-Line Selection of Discriminative Tracking Features,” Proc. IEEE Int'l Conf. Computer Vision, 2003.
[4] D. Comaniciu and P. Meer, “Mean Shift: A Robust Approach Toward Feature Space Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, 2002.
[5] D. Comaniciu, V. Ramesh, and P. Meer, “Real-Time Tracking of Non-Rigid Objects Using Mean Shift,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000.
[6] A. Elgammal, R. Duraiswami, and L. Davis, “Probabilistic Tracking in Joint Feature-Spatial Spaces,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2003.
[7] A. Elgammal, D. Harwood, and L. Davis, “Background and Foreground Modeling Using Non-Parametric Kernel Density Estimation for Visual Surveillance,” Proc. IEEE, 2002.
[8] L. Ford and D. Fulkerson, Flows in Networks. Princeton Univ. Press, 1962.
[9] N. Friedman and S. Russell, “Image Segmentation in Video Sequences: A Probabilistic Approach,” Proc. 13th Conf. Uncertainity in Artificial Intelligence, 1997.
[10] K. Fukunaga, Introduction to Statistical Pattern Recognition. Academic Press, 1990.
[11] S. Geman and D. Geman, “Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, 1984.
[12] D. Greig, B. Porteous, and A. Seheult, “Exact Maximum a Posteriori Estimation for Binary Images,” J. Royal Statistical Soc., vol. 51, 1989.
[13] P. Hall and M. Wand, “On the Accuracy of Binned Kernel Estimators,” J. Multivariate Analysis, 1995.
[14] I. Haritaoglu, D. Harwood, and L. Davis, “W4: Real-Time of People and Their Activities,” IEEE Trans. Pattern Analysis and Machine Intelligence, 2000.
[15] M. Harville, “A Framework of High-Level Feedback to Adaptive, Per-Pixel, Mixture of Gaussian Background Models,” Proc. European Conf. Computer Vision, 2002.
[16] M. Isard and A. Blake, “Condensation— Conditional Density Propagation for Visual Tracking,” Proc. Int'l J. Computer Vision, vol. 29, no. 1, pp. 5-28, 1998.
[17] R. Jain and H. Nagel, “On the Analysis of Accumulative Difference Pictures from Image Sequences of Real World Scenes,” IEEE Trans. Pattern Analysis and Machine Intelligence, 1979.
[18] O. Javed, K. Shafique, and M. Shah, “A Hierarchical Appraoch to Robust Background Subtraction Using Color and Gradient Information,” Proc. IEEE Workshop Motion and Video Computing, 2002.
[19] M. Jones, “Variable Kernel Density Estimates,” Austrailian J. Statistics, 1990.
[20] K.-P. Karmann, A. Brandt, and R. Gerl, “Using Adaptive Tracking to Classify and Monitor Activities in a Site,” Time Varying Image Processing and Moving Object Recognition, 1990.
[21] D. Koller, J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell, “Towards Robust Automatic Traffic Scene Analysis in Real-Time,” Proc. Int'l Conf. Pattern Recognition, 1994.
[22] V. Kolmogorov and R. Zabih, “What Energy Functions Can Be Minimized Via Graph Cuts?” IEEE Trans. Pattern Analysis and Machine Intelligence, 2004.
[23] S. Li, Markov Random Field Modeling in Computer Vision. Springer-Verlag, 1995.
[24] A. Mittal and N. Paragios, “Motion-Based Background Subtraction Using Adaptive Kernel Density Estimation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
[25] A. Monnet, A. Mittal, N. Paragios, and V. Ramesh, “Background Modeling and Subtraction of Dynamic Scenes,” IEEE Proc. Int'l Conf. Computer Vision, 2003.
[26] N. Oliver, B. Rosario, and A. Pentland, “A Bayesian Computer Vision System for Modeling Human Interactions,” IEEE Trans. Pattern Analysis and Machine Intelligence, 2000.
[27] E. Parzen, “On Estimation of a Probability Density and Mode,” Annals of Math. Statistics, 1962.
[28] R. Pless, J. Larson, S. Siebers, and B. Westover, “Evaluation of Local Models of Dynamic Backgrounds,” Proc. Conf. Computer Vision and Pattern Recognition, 2003.
[29] Y. Ren, C.-S. Chua, and Y.-K. Ho, “Motion Detection with Nonstationary Background,” Machine Vision and Application, Springer-Verlag, 2003.
[30] J. Rittscher, J. Kato, S. Joga, and A. Blake, “A Probabilistic Background Model for Tracking,” Proc. European Conf. Computer Vision, 2000.
[31] M. Rosenblatt, “Remarks on Some Nonparametric Estimates of a Density Functions,” Annals of Math. Statistics, 1956.
[32] S. Sain, “Multivariate Locally Adaptive Density Estimates,” Computational Statistics and Data Analysis, 2002.
[33] C. Stauffer and W. Grimson, “Learning Patterns of Activity Using Real-Time Tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence, 2000.
[34] B. Stenger, V. Ramesh, N. Paragios, F Coetzee, and J. Buhmann, “Topology Free Hidden Markov Models: Application to Background Modeling,” Proc. European Conf. Computer Vision, 2000.
[35] K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: Principles and Practice of Background Maintenance,” IEEE Proc. Int'l Conf. Computer Vision, 1999.
[36] B. Turlach, “Bandwidth Selection in Kernel Density Estimation: A Review,” Institut für Statistik und Ökonometrie, Humboldt-Universität zu Berlin, 1993.
[37] T. Wada and T. Matsuyama, “Appearance Sphere: Background Model for Pan-Tilt-Zoom Camera,” Proc. Int'l Conf. Pattern Recognition, 1996.
[38] M. Wand and M. Jones, “Kernel Smoothing,” Monographs on Statistics and Applied Probability, Chapman and Hill, 1995.
[39] C. Wren, A. Azarbayejani, T. Darrel, and A. Pentland, “Pfinder: Real Time Tracking of the Human Body,” IEEE Trans. Pattern Analysis and Machine Intelligence, 1997.
[40] J. Zhong and S. Sclaroff, “Segmenting Foreground Objects from a Dynamic Textured Background Via a Robust Kalman Filter,” IEEE Proc. Int'l Conf. Computer Vision, 2003.

Index Terms:
Index Terms- Object detection, kernel density estimation, joint domain range, MAP-MRF estimation.
Citation:
Yaser Sheikh, Mubarak Shah, "Bayesian Modeling of Dynamic Scenes for Object Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 11, pp. 1778-1792, Nov. 2005, doi:10.1109/TPAMI.2005.213
Usage of this product signifies your acceptance of the Terms of Use.