Multi-frame Motion Segmentation by Combining Two-Frame Results

In this paper we consider the motion segmentation problem on sparse and unstructured datasets involving rigid motions, motivated by multibody structure from motion. In particular, we assume only two-frame correspondences as input without prior knowledge about trajectories. Inspired by the success of...

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Bibliographic Details
Published in:International journal of computer vision 2022-03, Vol.130 (3), p.696-728
Main Authors: Arrigoni, Federica, Ricci, Elisa, Pajdla, Tomas
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Imaging
Computer Science
Datasets
Image Processing and Computer Vision
Image segmentation
Pattern Recognition
Pattern Recognition and Graphics
Special Issue on 3D Computer Vision
Synchronism
Vision
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Summary:In this paper we consider the motion segmentation problem on sparse and unstructured datasets involving rigid motions, motivated by multibody structure from motion. In particular, we assume only two-frame correspondences as input without prior knowledge about trajectories. Inspired by the success of synchronization methods, we address this problem by introducing a two-stage approach: first, motion segmentation is addressed on image pairs independently; then, two-frame results are combined in a robust way to compute the final multi-frame segmentation. Our synthetic and real experiments demonstrate that the proposed approach is very effective in reducing the errors among two-frame results and it can cope with a large amount of mismatches. Moreover, our method can be profitably used to build a multibody structure from motion pipeline.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-021-01544-x