NDLI: Superpixel-based large displacement optical flow

Content Provider	IEEE Xplore Digital Library
Author	Haw-Shiuan Chang Wang, Y.-C.F.
Copyright Year	2013
Description	Author affiliation: Res. Center for Inf. Technol. Innovation, Taipei, Taiwan (Haw-Shiuan Chang; Wang, Y.-C.F.)
Abstract	It has been a challenging task to estimate optical flow for videos in which either foreground or background exhibits remarkable motion information (i.e., large displacement), or those with insufficient resolution due to artifacts like motion blur or noise. We present a novel optical flow algorithm, which approaches the above problem as solving the task of energy minimization, which exploits image data and smoothness terms at the superpixel level. Our proposed method can be considered as an extended mean-shift algorithm, which advances color and gradient information of superpixels across consecutive frames with smoothness guarantees. Since we do not require assumptions of linearlization during optimization (as standard optical flow approaches do), we are able to alleviate local minimum problems and thus produce improved estimation results. Empirical results on the MPI-Sintel video dataset verify the effectiveness of our proposed method.
Starting Page	3835
Ending Page	3839
File Size	3169545
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738790
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	mean shift large displacement optical flow superpixel
Content Type	Text
Resource Type	Article

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