Open AccessAdaptive dual fractional‐order variational optical flow model for motion estimation
Author(s) -
Zhu Bin,
Tian LianFang,
Du QiLiang,
Wu QiuXia,
Sahl Farisi Zeyad,
Yeboah Yao
Publication year - 2019
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5285
Subject(s) - optical flow , computer science , dual (grammatical number) , noise (video) , computer vision , differential (mechanical device) , artificial intelligence , algorithm , smoothness , flow (mathematics) , mathematics , image (mathematics) , geometry , art , literature , mathematical analysis , engineering , aerospace engineering
Insufficient illumination and illumination variation in image sequences make it challenging for algorithms to obtain clear outlines for objects in motion. This study proposes a high‐performance adaptive dual fractional‐order variational optical flow model which could be used to resolve these issues. The proposed method revitalises the original dual fractional‐order optical flow model and adopts a fractional differential mask in both the data and smoothness terms of the traditional Horn–Schunck model. The main innovation of this work is to fit a flow field regional to a variety of fractional‐order differential masks. The domain of each region is determined adaptively. The order and size of the fractional‐order differential masks for each region are adjusted by image signal to noise ratio while the shape of the fractional‐order differential mask is regulated to prevent interference from surrounding regions. Adjusting the fractional‐order differential mask adaptively enables the proposed method to accurately segment motion objects in poor and variable illumination regions as well. The experimental results show that our algorithm outperforms the current state‐of‐the‐art algorithms on low‐light real scene videos and also achieves competitive results on the Middlebury, KITTI and MPI Sintel public benchmarks.