NDLI: Adaptive dual fractional-order variational optical flow model for motion estimation

Content Provider	IET Digital Library
Author	Zhu, Bin Tian, Lian Fang Du, Qi Liang Wu, Qiu Xia Sahl, Farisi Zeyad Yeboah, Yao
Abstract	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.
Starting Page	277
Ending Page	284
Page Count	8
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 3, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5285
Journal	IET Computer Vision
Publisher Date	2018-09-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Fractional Differential Mask Fractional-order Differential Mask High-performance Adaptive Dual Fractional-order Variational Optical Flow Model Horn–Schunck Model Illumination Variation Image Segmentation Image Sequence Low-light Real Scene Videos Motion Estimation Optical, Image And Video Signal Processing Smoothness Term
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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