Open AccessMotion of moving camera from point matches: comparison of two robust estimation methods
Author(s) -
Horemuz Milan,
Zhao Yueming
Publication year - 2014
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2013.0271
Subject(s) - ransac , outlier , estimator , artificial intelligence , mathematics , computer science , robust statistics , motion estimation , noise (video) , structure from motion , computer vision , robustness (evolution) , statistics , image (mathematics) , biochemistry , chemistry , gene
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.