NDLI: Motion of moving camera from point matches: comparison of two robust estimation methods

Content Provider	IET Digital Library
Author	Horemuz, Milan Zhao, Yueming
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.
Starting Page	682
Ending Page	689
Page Count	8
ISSN	17519632
Volume Number	8
e-ISSN	17519640
Issue Number	Issue 6, Dec (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/8/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2013.0271
Journal	IET Computer Vision
Publisher Date	2014-05-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Balanced Least Absolute Value Estimator Best-fit Parameter Estimation BLAVE Camera Camera Motion Parameter Estimation Problem Computer Vision And Image Processing Technique Gross Error Image Matching Image Recognition Image Sonsor Linearised Model Median-like Property Mobile Mapping System Motion Estimation Point Matches Random Noise Random Processes Random Sample Consensus Method RANSAC Method Robust Estimation Method Statistics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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