Bayesian modeling of dynamic scenes for object detection

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 27, Issue 11, 2005, Pages 1778-1792

  Sheikh, Yaser ^a   Shah, Mubarak ^a  

^a University of Central Florida   (United States)

Author keywords

Joint domain range; Kernel density estimation; MAP MRF estimation; Object detection

Indexed keywords

CORRELATION METHODS; MATHEMATICAL MODELS; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS; RANDOM PROCESSES;

JOINT DOMAIN RANGE; KERNEL DENSITY ESTIMATION; OBJECT DETECTION;

OBJECT RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; EVALUATION; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; METHODOLOGY; STATISTICAL MODEL; VIDEORECORDING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; SUBTRACTION TECHNIQUE; VIDEO RECORDING;

EID: 28044439637     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2005.213     Document Type: Article

References (40)

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. Uncertainty 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 Approach to Robust Background Subtraction Using Color and Gradient Information," Proc. IEEE Workshop Motion and Video Computing, 2002.
19. M. Jones, "Variable Kernel Density Estimates," Australian 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 Okonometrie, 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.