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ORB: An efficient alternative to SIFT or SURF

Publisher: IEEE

Abstract:
Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone.
Date of Conference: 06-13 November 2011
Date Added to IEEE Xplore: 12 January 2012
ISBN Information:
ISSN Information:
Publisher: IEEE
Conference Location: Barcelona, Spain

1. Introduction

The SIFT keypoint detector and descriptor [17], although over a decade old, have proven remarkably successful in a number of applications using visual features, including object recognition [17], image stitching [28], visual mapping [25], etc. However, it imposes a large computational burden, especially for real-time systems such as visual odometry, or for low-power devices such as cellphones. This has led to an intensive search for replacements with lower computation cost; arguably the best of these is SURF [2]. There has also been research aimed at speeding up the computation of SIFT, most notably with GPU devices [26].

References

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