DocumentCode :
3672167
Title :
Dense, accurate optical flow estimation with piecewise parametric model
Author :
Jiaolong Yang;Hongdong Li
Author_Institution :
Beijing Lab of Intelligent Information Technology, Beijing Institute of Technology, China
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
1019
Lastpage :
1027
Abstract :
This paper proposes a simple method for estimating dense and accurate optical flow field. It revitalizes an early idea of piecewise parametric flow model. A key innovation is that, we fit a flow field piecewise to a variety of parametric models, where the domain of each piece (i.e., each piece´s shape, position and size) is determined adaptively, while at the same time maintaining a global inter-piece flow continuity constraint. We achieve this by a multi-model fitting scheme via energy minimization. Our energy takes into account both the piecewise constant model assumption and the flow field continuity constraint, enabling the proposed method to effectively handle both homogeneous motions and complex motions. The experiments on three public optical flow benchmarks (KITTI, MPI Sintel, and Middlebury) show the superiority of our method compared with the state of the art: it achieves top-tier performances on all the three benchmarks.
Keywords :
"Parametric statistics","Estimation","Labeling","Benchmark testing","Motion segmentation","Adaptation models","Image segmentation"
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2015.7298704
Filename :
7298704
Link To Document :
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