Motion Detail Preserving Optical Flow Estimation

Authors:
Li Xu;Jiaya Jia;Yasuyuki Matsushita
Affiliations:
The Chinese University of Hong Kong, Hong Kong;The Chinese University of Hong Kong, Hong Kong;Microsoft Research Asia, Beijing
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2012

Citing 0
Cited 4

Histogram-Based Optical Flow for Motion Estimation in Ultrasound Imaging

Journal of Mathematical Imaging and Vision
Adaptive large window correlation for optical flow estimation with discrete optimization

Image and Vision Computing
A combined post-filtering method to improve accuracy of variational optical flow estimation

Pattern Recognition
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them

International Journal of Computer Vision

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Abstract

A common problem of optical flow estimation in the multiscale variational framework is that fine motion structures cannot always be correctly estimated, especially for regions with significant and abrupt displacement variation. A novel extended coarse-to-fine (EC2F) refinement framework is introduced in this paper to address this issue, which reduces the reliance of flow estimates on their initial values propagated from the coarse level and enables recovering many motion details in each scale. The contribution of this paper also includes adaptation of the objective function to handle outliers and development of a new optimization procedure. The effectiveness of our algorithm is demonstrated by Middlebury optical flow benchmarkmarking and by experiments on challenging examples that involve large-displacement motion.