Sparse motion segmentation using multiple six-point consistencies
Computer Vision and Pattern Recognition
2010-12-14 v2
Abstract
We present a method for segmenting an arbitrary number of moving objects in image sequences using the geometry of 6 points in 2D to infer motion consistency. The method has been evaluated on the Hopkins 155 database and surpasses current state-of-the-art methods such as SSC, both in terms of overall performance on two and three motions but also in terms of maximum errors. The method works by finding initial clusters in the spatial domain, and then classifying each remaining point as belonging to the cluster that minimizes a motion consistency score. In contrast to most other motion segmentation methods that are based on an affine camera model, the proposed method is fully projective.
Cite
@article{arxiv.1012.2138,
title = {Sparse motion segmentation using multiple six-point consistencies},
author = {Vasileios Zografos and Klas Nordberg and Liam Ellis},
journal= {arXiv preprint arXiv:1012.2138},
year = {2010}
}