摘要

转自：视觉机器人

现在，机器学习的趋势从传统方法中的简单模型 + 少量数据（人工标

一种视频的快速搜索技术，比SIFT还厉害。基于网格的运动统计，用于快速、超鲁棒的特征匹配（办公椅演示），论文《Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence》将平滑度约束引入特征匹配是已知的可以实现超强鲁棒匹配。 然而，这样的匹配方案既复杂又缓慢，使得它们不适合于视频应用。 本文提出了GMS（基于网格的运动统计），一种简单的方法，将运动平滑度作为一个统计量，进行局部区域的匹配。GMS可以将高匹配数字转换成高匹配质量。 这提供了一个实时、超强的匹配系统。 评估低质量、模糊的视频和广泛基线显示，GMS始终如一地优于其他实时匹配器。

摘要：

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.

项目主页：

http://jwbian.net/gms

原文链接：

2019/20190426A/F0110084

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