Bayesian algorithms for simultaneous structure from motion estimation of multiple independently moving objects

Gang Qian, Raman Chellappa, Qinfen Zheng

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, the problem of simultaneous structure from motion estimation for multiple independently moving objects from a monocular image sequence is addressed. Two Bastion algorithms are presented for solving this problem using the sequential importance sampling (SIS) technique. The empirical posterior distribution of object motion and feature separation parameters is approximated by weighted samples. The first algorithm addresses the problem when only two moving objects are present. A singular value decomposition (SVD)-based sample clustering algorithm is shown to be capable of separating samples related to different objects. A pair of SIS procedures is used to track the posterior distribution of the motion parameters. In the second 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 algorithms, the relative motions of all the moving objects with respect to the camera can be simultaneously estimated. Both algorithms have been tested on synthetic and real-image sequences. Improved results have been achieved.

Original languageEnglish (US)
Pages (from-to)94-109
Number of pages16
JournalIEEE Transactions on Image Processing
Volume14
Issue number1
DOIs
StatePublished - Jan 2005
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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