A bayesian approach to simultaneous motion estimation of multiple independently moving objects

In this paper, the problem of simultaneous motion estimation of multiple independently moving objects is addressed. A novel Bayesian approach is designed for solving this problem using the sequential importance sampling (SIS) method. In the proposed algorithm, a balancing step is added into the SIS procedure to preserve samples of low weights so that all objects have enough samples to propagate empirical motion distributions. By using the proposed algorithm, the relative motions of all moving objects with respect to camera can be simultaneously estimated . This algorithm has been tested on both synthetic and real image sequences. Improved results have been achieved.

[1]  P. Perona,et al.  Three dimensional transparent structure segmentation and multiple 3D motion estimation from monocular perspective image sequences , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[2]  Takeo Kanade,et al.  A Multibody Factorization Method for Independently Moving Objects , 1998, International Journal of Computer Vision.

[3]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[5]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[6]  Rama Chellappa,et al.  Moving targets detection using sequential importance sampling , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[7]  Amnon Shashua,et al.  Multi-frame infinitesimal motion model for the reconstruction of (dynamic) scenes with multiple linearly moving objects , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Alex Pentland,et al.  Recursive Estimation of Motion, Structure, and Focal Length , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Pietro Perona,et al.  Motion Estimation on the Essential Manifold , 1994, ECCV.

[10]  P PentlandAlex,et al.  Recursive Estimation of Motion, Structure, and Focal Length , 1995 .

[11]  Mei Han,et al.  Reconstruction of a Scene with Multiple Linearly Moving Objects , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).