Modeling and estimating persistent motion with geometric flows

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 1783-1790

  Lin, Dahua ^a   Grimson, Eric ^a   Fisher, John ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COLLECTIVE DYNAMICS; DYNAMIC SCENES; FRAME DIFFERENCES; GAUSSIAN PROCESSES; GEOMETRIC FLOWS; GLOBAL MODELS; LIE ALGEBRA; LOCAL MOTIONS; MODEL COMPLEXES; MULTIPLE FLOWS; OBJECT TRACKING; OPTICAL FLOW ESTIMATION; ROBUST ALGORITHM; SPACE AND TIME; SPATIAL COHERENCE; STOCHASTIC FLOW MODEL; VECTOR SPACE REPRESENTATION;

COMPUTER VISION; ESTIMATION; MOTION ESTIMATION; OPTICAL FLOWS; VECTOR SPACES;

STOCHASTIC MODELS;

EID: 77956003887     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539848     Document Type: Conference Paper

References (20)

1. S. Ali and M. Shah. A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In Proc. of CVPR'07, 2007.
2. S. Ali and M. Shah. Floor fields for tracking in high density crowd scenes. In ECCV'08, 2008.
3. T. Amiaz, S. Fazekas, D. Chetverikov, and N. Kiryati. Detecting regions of dynamic textures. In Conf. on Scale Space and Variational Methods in Computer Vision, 2007.
4. Bastian, K. Schindler, and L. V. Gool. Coupled detection and trajectory estimation for multi-object tracking. In Proc. of ICCV'07, 2007.
5. Y. Boycov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. IEEE Trans. on PAMI, 23(11):1222-1239, 2001.
6. T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. In Proc. of ECCV'04, 2004.
7. A. B. Chan and N. Vasconcelos. Modeling, clustering, and segmenting video with mixtures of dynamic textures. IEEE Trans. on PAMI, 30(5):909-926, May 2008.
8. N. Jacobs, M. Dixon, and R. Pless. Location-specific transition distributions for tracking. In WMVC'08, 2008.
9. J. M. Lee. Introduction to Smooth Manifolds. Springer, 2002.
10. D. Lin, E. Grimson, and J. Fisher. Learning visual flows: A lie algebraic approach. In CVPR'09, 2009.
11. D. Lowe. Distinctive image features from scale-invariant keypoints. Int'l J. of Computer Vision, 60(2), 2004.
12. B. D. Lucas and T. Kanade. An iterative image registration technique with an application in stereo vision. In IJCAI, 1981.
13. H. T. Nguyen, Q. Ji, and A. W. Smeulders. Spatio-temporal context for robust multitarget tracking. IEEE Trans. on PAMI, 29(1):52-64, Jan. 2007.
14. N. Papadakis, T. Corpetti, and E. Memin. Dynamically consistent optical flow estimation. In Proc. of ICCV'07, 2007.
15. C. E. Rasmussen and C. K. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
16. X. Ren. Local grouping for optical flow. In Proc. of CVPR'08, 2008.
17. S. Roth and M. J. Black. On the spatial statistics of optical flow. In Proc. of ICCV'05, 2005.
18. T. Schoenemann and D. Cremers. High resolution motion layer decomposition using dual-space graph cut. In Proc. of CVPR'08, 2008.
19. X. Wang, K. T. Ma, G.-W. Ng, and E. Grimson. Trajectory analysis and semantic region modeling using a nonparametric bayesian model. In Proc. of CVPR'08, 2008.
20. T. Zhao, R. Nevatia, and B. Wu. Segmentation and tracking of multiple humans in crowded environments. IEEE Trans. on PAMI, 30(7):1198-1211, July 2008.