In order to improve the accuracy and robustness of optical flow computation under large displacements and motion occlusions, the authors present in this study a large displacement flow field estimation approach using similarity transformation-based dense correspondence, named STDC-Flow approach. First, the authors compute an initial nearest-neighbour field by using the STDC-Flow of the consecutive two frames, and then extract the consistent regions as the robust nearest-neighbour field and label the inconsistent regions as the occlusion areas. Second, they improve a non-local total variation with the L 1 norm optical flow model by using the occlusion information to modify the weighted median filtering optimisation. Third, they fuse the robust nearest-neighbour field and the computed flow field of the improved variational optical flow model to construct the final flow field by using the quadratic pseudo-boolean optimisation fusion algorithm. Finally, the authors compare the proposed STDC-Flow method with several state-of-the-art approaches including the variational and deep learning-based optical flow models by using the MPI-Sintel and KITTI evaluation databases. The comparison results demonstrate that the proposed STDC-Flow method has a high accuracy for flow field computation, especially the capacity of dealing with large displacements and motion occlusions.

中文翻译：

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STDC-Flow：使用基于相似变换的密集对应关系进行大位移流场估计

为了提高大位移和运动遮挡下光流计算的准确性和鲁棒性，本研究的作者提出了一种大位移流场估计方法，该方法使用基于相似度变换的密集对应关系，称为STDC-Flow方法。首先，作者使用连续两个帧的STDC-Flow计算初始最近邻场，然后提取一致区域作为鲁棒最近邻场，并将不一致区域标记为遮挡区域。其次，他们通过大号 通过使用遮挡信息来修改加权中值滤波优化，从而获得1范数光流模型。第三，他们使用二次伪布尔优化融合算法融合了稳健的最近邻场和改进的变分光流模型的计算流场，以构建最终流场。最后，作者们通过使用MPI-Sintel和KITTI评估数据库，将提议的STDC-Flow方法与几种最新方法进行了比较，包括基于变分和深度学习的光流模型。比较结果表明，所提出的STDC-Flow方法具有较高的流场计算精度，特别是处理大位移和运动闭塞的能力。