Motion of moving camera from point matches: comparison of two robust estimation methods. Issue 6 (1st December 2014)

• Record Type: Journal Article
• Authors: Horemuz, Milan; Zhao, Yueming
• Is Part Of: IET computer vision. Volume 8:Issue 6(2014)
• Page Start: 682
• Page End: 689
• Publication Date: 2014-12-01
• Subjects: motion estimation -- cameras -- image matching -- random noise -- random processes; point matches -- robust estimation methods -- balanced least absolute value estimator -- BLAVE -- random sample consensus method -- RANSAC method -- camera motion parameter estimation problem -- linearised model -- random noise -- gross errors -- mobile mapping system -- median-like property -- best-fit parameter estimation; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2013.0271
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : A robust estimation method, Balanced Least Absolute Value Estimator (BLAVE), is introduced and compared with the traditional RANdom SAmple Consensus (RANSAC) method. The comparison is performed empirically by applying both estimators on the camera motion parameters estimation problem. A linearised model for this estimation problem is derived. The tests were performed on a simulated scene with added random noise and gross errors as well as on actual images taken by a mobile mapping system. The greatest advantage of BLAVE is that it processes all observations at once as well as its median‐like property: the estimated parameters are not influenced by the size of the outliers. It can tolerate up to 50% outliers in data and still produce accurate results. The greatest disadvantage of RANSAC is that the results are not repeatable because of the random sampling of data. Moreover, the results are less accurate, because RANSAC generally does not produce a 'best‐fit' parameter estimation. The number of trials, which must be tested by RANSAC to find a reasonable solution, depends on the portion of outliers in data. The computational time for BLAVE does not depend on the portion of outliers in the observations, but it grows with the number of observations, same as RANSAC.