Adaptive dual fractional‐order variational optical flow model for motion estimation

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.

An Adaptive Smoothness Parameter Strategy for Variational Optical Flow Model

“…ree public databases: Middlebury [49], MPI_Sintel [50], KITTI [51] and several outdoor videos are used in these experiments. Our proposed algorithm is compared with DFOVOFM [36], TV_L1 [52], HAST [53], MDP_Flow [54], PH_Flow [55], and WRMS based smoothness parameter selection method [27]. All the simulation results are performed on MATALB 10.0 with system configuration of Windows 7, Intel 3.3 GHZ, and 16 GB RAM.…”

“…Flow Model e dual fractional order optical flow model (DFOVOFM) [36] is a fractional order version of the HS model in which the data term and smoothness term of the HS model are reconstructed by fractional order derivative. e predominance of DFOVOFM is that the model integrated the variation characteristics around the target points, which not only retains the edge and texture details, but also eliminates the influence of subtle noise, so as to improve the robustness of the model to environmental changes in the process of optical flow estimation.…”

Large Displacement Optical Flow Estimation Based on Robust Interpolation of Sparse Correspondences

“…Some studies proposed a local illumination change model [28] to deal with the weakly textured scenes. Furthermore, an adaptive dual fractional-order variational optical flow method [29] was presented to solve the issues of insufficient illumination and illumination changes. Despite that the accuracy and robustness of variational optical flow estimation have been significantly improved, these variational methods usually require a mass of iterations to minimize the objective function.…”