Experimental Study Efficiency of Robust Models of Lucas-Kanade Optical Flow Algorithms in the Present of Non-Gaussian Noise
Experimental Study Efficiency of Robust Models of Lucas-Kanade Optical Flow Algorithms in the Present of Non-Gaussian Noise
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2012-07
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eng
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application/pdf
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6 pages
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Proceedings of the 4th International Conference on Knowledge and Smart Technologies (KST-2012), Burapha University, Chonburi, Thailand, July 7-8, 2012. (IEEE Xplore)
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Abstract
This paper presents experimental efficiency study
of noise tolerance model of spatial optical flow based on LucasKanade
(LK) algorithms such as original LK with kernel of
Barron, Fleet, and Beauchemin (BFB), confidence based optical
flow algorithm for high reliability (CRR), robust motion
estimation methods using gradient orientation information
(RGOI), and a novel robust and high reliability for LucasKanade
optical flow algorithm using median filter and
confidence based technique (NRLK) under several NonGaussian
Noise. These experiment results are comprehensively
tested on several standard sequences (such as AKIYO,
COASTGUARD, CONTAINER, and FOREMAN) that have
differences speed, foreground and background movement
characteristics in a level of 0.5 sub-pixel displacements. Each
standard sequence has 6 sets of sequence included an original
(no noise), Poisson Noise (PN), Salt&Pepper Noise (SPN) at
density (d) = 0.005 and d = 0.025, Speckle Noise (SN) at variance
(v) = 0.01 and v = 0.05 respectively which Peak Signal to Noise
Ratio (PSNR) is concentrated as the performance indicator.