Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2017

Abstract

Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching. However, such formulations are both complex and slow, making them unsuitable for video applications. This paper proposes GMS (Grid-based Motion Statistics), a simple means of encapsulating motion smoothness as the statistical likelihood of a certain number of matches in a region. GMS enables translation of high match numbers into high match quality. This provides a real-time, ultra-robust correspondence system. Evaluation on videos, with low textures, blurs and wide-baselines show GMS consistently out-performs other real-time matchers and can achieve parity with more sophisticated, much slower techniques.

Discipline

Computer and Systems Architecture

Publication

Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2017, Honolulu, USA, July 21-26

First Page

2828

Last Page

2837

ISBN

1063-6919

Identifier

10.1109/CVPR.2017.302

Publisher

IEEE

City or Country

Honolulu, USA

Additional URL

https://doi.org/10.1109/CVPR.2017.302

Share

COinS