Approximate Bayesian multibody tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 28, Issue 9, 2006, Pages 1436-1449

  Lanz, Oswald ^a  

^a ITC IRST   (Italy)

Author keywords

Approximate inference; Bayes filter; Computer vision; Occlusion; Particle filter; Tracking

Indexed keywords

COMPUTER SIMULATION; INFERENCE ENGINES; MARKOV PROCESSES; PROBABILITY DISTRIBUTIONS; STATISTICAL METHODS;

APPROXIMATE INFERENCE; BAYESIAN MULTIBODY TRACKING; COMPUTATIONAL EFFICIENCY; OCCLUSIONS; PARTICLE FILTER;

COMPUTER VISION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; METHODOLOGY; MOVEMENT (PHYSIOLOGY); REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MOVEMENT; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 33746486814     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2006.177     Document Type: Article

References (36)

1. O. Lanz and R. Manduchi, "Hybrid Joint-Separable Multibody Tracking," Proc. Int'l Conf. Computer Vision and Pattern Recognition, 2005.
2. J. MacCormick and A. Blake, "Probabilistic Exclusion and Partitioned Sampling for Tracking Multiple Objects," Int'l J. Computer Vision, vol. 39, no. 1, 2000.
3. D. Tweed and A. Calway, "Tracking Many Objects Using Subordinated CONDENSATION," Proc. British Machine Vision Conf., 2002.
4. H. Tao, H.S. Sawhney, and R. Kumar, "A Sampling Algorithm for Detecting and Tracking Multiple Objects," Proc. Vision Algorithms Workshop, 1999.
5. K. Otsuka and N. Mukawa, "Multiview Occlusion Analysis for Tracking Densely Populated Objects Based on 2-D Visual Angles," Proc. Int'l Conf. Computer Vision and Pattern Recognition, 2004.
6. B. Vo, S. Singh, and A. Doucet, "Sequential Monte Carlo Methods for Bayesian Multi-Target Filtering with Random Finite Sets," IEEE Trans. Aerospace and Electronic Systems, vol. 41, no. 4, 2005.
7. M. Isard and J. MacCormick, "BraMBle: A Bayesian Multiple-Blob Tracker," Proc. Int'l Conf. Computer Vision, 2003.
8. J. Sullivan, A. Blake, M. Isard, and J. MacCormick, "Bayesian Object Localisation in Images," Int'l J. Computer Vision, vol. 44, no. 2, 2001.
9. J. Vermaak, A. Doucet, and P. Perez, "Maintaining Multi-Modality through Mixture Tracking," Proc. Int'l Conf. Computer Vision, 2001.
10. Z. Khan, T. Balch, and F. Dellaert, "MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 11, Nov. 2005.
11. T. Yu and Y. Wu, "Collaborative Tracking of Multiple Targets," Proc. Int'l Conf. Computer Vision and Pattern Recognition, 2005.
12. J. Deutscher, A. Blake, and I. Reid, "Articulated Body Motion Capture by Annealed Particle Filtering," Proc. Int'l Conf. Computer Vision and Pattern Recognition, 2000.
13. C. Sminchisescu and B. Triggs, "Fast Mixing Hyperdynamic Sampling," J. Image and Vision Computing, vol. 24, no. 3, 2006.
14. K. Choo and D. Fleet, "people Tracking with Hybrid Monte Carlo," Proc. Int'l Conf. Computer Vision, 2001.
15. J. Sullivan and J. Rittscher, "Guiding Random Particles by Deterministic Search," Proc. Int'l Conf. Computer Vision, 2001.
16. C. Sminchisescu and B. Triggs, "A Robust Multiple Hypothesis Approach to Monocular Human Motion Tracking," Technical Report 4208, INRIA, 2001.
17. M. Cover and J. Thomas, Elements of Information Theory. Wiley, 1991.
18. J. Coughlan and S. Ferreira, "Finding Deformable Shapes Using Loopy Belief Propagation," Proc. European Conf. Computer Vision, 2002.
19. J. Sun, H. Shum, and N. Zheng, "Stereo Matching Using Belief Propagation," Proc. European Conf. Computer Vision, 2002.
20. L. Sigal, S. Bhatia, S. Roth, M. Black, and M. Isard, "Tracking Loose-Limbed People," Proc. Int'l Conf. Computer Vision and Pattern Recognition, 2004.
21. E. Sudderth, M. Mandel, W. Freeman, and A. Willsky, "Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation," Advances in Neural Information Processing Systems, MIT Press, 2005.
22. J. Yedidia, W. Freeman, and Y. Weiss, "Understanding Belief Propagation and Its Generalizations," Exploring Artificial Intelligence in the New Millennium, Morgan Kaufmann, 2003.
23. M. Isard, "PAMPAS: Real-Valued Graphical Models for Computer Vision," Proc. Int'l Conf. Computer Vision and Pattern Recognition, 2003.
24. E. Sudderth, A. Ihler, W. Freeman, and A. Willsky, "Nonparametric Belief Propagation," Proc. Int'l Conf. Computer Vision and Pattern Recognition, 2003.
25. K. Murphy, "Dynamic Bayesian Networks: Representation, Inference and Learning," PhD dissertation, Univ. of California, Berkeley, 2002.
26. Q. Diao, J. Lu, W. Hu, Y. Zhang, and G. Bradski, "DBN Models and a Prediction Method for Visual Tracking," Proc. Bayesian Modeling Applications Workshop, 2003.
27. M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul, "An Introduction to Variational Methods for Graphical Models," Machine Learning, 1999.
28. M. Opper and D. Saad, Advanced Mean Field Methods. MIT Press, 2001.
29. S. Li, Markov Random Field Modeling in Computer Vision. Springer, 1995.
30. A. Santuari, O. Lanz, and R. Brunelli, "Synthetic Movies for Computer Vision Applications," Proc. Int'l Conf. Visualization, Imaging, and Image Processing, 2003.
31. H. Sidenbladh and M. Black, "Learning the Statistics of People in Images and Video," Int'l J. Computer Vision, vol. 54, no. 1, 2003.
32. A. Jepson, D. Fleet, and T. El-Maraghi, "Robust Online Appearance Models for Visual Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 10, Oct. 2003.
33. D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-Based Object Tracking,'" IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 5, May 2003.
34. A. Doucet, N. DeFreitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. Springer, 2001.
35. M. Isard and A. Blake, "CONDENSATION-Conditional Density Propagation for Visual Tracking," Int'l J. Computer Vision, vol. 29, no. 1, 1998.
36. S. Arulampalam, A. Maskell, N. Gordon, and T. Clapp, "A Tutorial on Particle Filters for On-Line Non-Linear/Non-Gaussian Bayesian Tracking," IEEE Trans. Signal Processing, vol. 50, no. 2, 2002.