GMS: Grid-based Motion Statistics for Fast, Ultra-Robust Feature Correspondence

JiaWang Bian, Wen-Yan Lin, Yasuyuki Matsushita, Sai-Kit Yeung, Tan-Dat Nguyen, Ming-Ming Cheng; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4181-4190

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.

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[bibtex]
@InProceedings{Bian_2017_CVPR,
author = {Bian, JiaWang and Lin, Wen-Yan and Matsushita, Yasuyuki and Yeung, Sai-Kit and Nguyen, Tan-Dat and Cheng, Ming-Ming},
title = {GMS: Grid-based Motion Statistics for Fast, Ultra-Robust Feature Correspondence},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}