Determining Occlusions From Space and Time Image Reconstructions

Juan-Manuel Perez-Rua, Tomas Crivelli, Patrick Bouthemy, Patrick Perez; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1382-1391

Abstract


The problem of localizing occlusions between consecutive frames of a video is important but rarely tackled on its own. In most works, it is tightly interleaved with the computation of accurate optical flows, which leads to a delicate chicken-and-egg problem. With this in mind, we propose a novel approach to occlusion detection where visibility or not of a point in next frame is formulated in terms of visual reconstruction. The key issue is now to determine how well a pixel in the first image can be "recon- structed" from co-located colors in the next image. We first exploit this reasoning at the pixel level with a new detection criterion. Contrary to the ubiquitous displaced-frame-difference and forward-backward flow vector matching, the proposed alternative does not critically depend on a precomputed, dense displacement field, while being shown to be more effective. We then leverage this local modeling within an energy-minimization framework that delivers occlusion maps. An easy-to-obtain collection of parametric motion models is exploited within the energy to provide the required level of motion information. Our approach outperforms state-of-the-art detection methods on the challenging MPI Sintel dataset.

Related Material


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[bibtex]
@InProceedings{Perez-Rua_2016_CVPR,
author = {Perez-Rua, Juan-Manuel and Crivelli, Tomas and Bouthemy, Patrick and Perez, Patrick},
title = {Determining Occlusions From Space and Time Image Reconstructions},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}