Abstract
The dense optical flow estimation under occlusion is a challenging task. Occlusion may result in ambiguity in optical flow estimation, while accurate occlusion detection can reduce the error. In this paper, we propose a robust optical flow estimation algorithm with reliable occlusion detection. Firstly, the occlusion areas in successive video frames are detected by integrating various information from multiple sources including feature matching, motion edges, warped images and occlusion consistency. Then optimization function with occlusion coefficient and selective region smoothing are used to obtain the optical flow estimation of the non-occlusion areas and occlusion areas respectively. Experimental results show that the algorithm proposed in this paper is an effective algorithm for dense optical flow estimation.
Highlights
The concept of optical flow, describing the apparent motion between images or video frames, arises from the studies of biological visual systems
Warped image is the intermediate result of the coarse to fine warping algorithm, from which we found the trace of occlusion creatively
We will propose our occlusion detection strategies based on various information, and our fusion strategy will be proposed in the last subsection
Summary
The concept of optical flow, describing the apparent motion between images or video frames, arises from the studies of biological visual systems. Most of the above methods do not deal with occlusion directly, instead they treat them as the outliers and suppress the influence of occlusion by introducing complex penalty terms, such as the Lorentzian potentials [17] and the Charbonnier potentials [18] This is the compromise using the consistency constraints in the whole image. Matching can provide the most basic occlusion detection results, which do not depend on optical flow Because it often only uses the simple features, it may appear false positive phenomenon, which may be invalid in edge areas or illumination changes. Based on the discrimination of occlusion and non-occlusion areas, we improve the efficiency and accuracy of the optimization function, and reduce the influence of smoothing operation on the results, obtaining better optical flow estimation results.
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