Bayesian self-calibration of a moving camera

TitleBayesian self-calibration of a moving camera
Publication TypeJournal Articles
Year of Publication2004
AuthorsQian G, Chellappa R
JournalComputer Vision and Image Understanding
Pagination287 - 316
Date Published2004/09//
ISBN Number1077-3142
Keywordsself-calibration, Sequential Monte Carlo methods, structure from motion, video analysis

In this paper, a Bayesian self-calibration approach using sequential importance sampling (SIS) is proposed. Given a set of feature correspondences tracked through an image sequence, the joint posterior distributions of both camera extrinsic and intrinsic parameters as well as the scene structure are approximated by a set of samples and their corresponding weights. The critical motion sequences are explicitly considered in the design of the algorithm. The probability of the existence of the critical motion sequence is inferred from the sample and weight set obtained from the SIS procedure. No initial guess for the calibration parameters is required. The proposed approach has been extensively tested on both synthetic and real image sequences and satisfactory performance has been observed.