ORB: an efficient alternative to SIFT or SURF
Pratiksha Jadhav
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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.
Key takeaways
- Descriptors BRIEF [6] is a recent feature descriptor that uses simple binary tests between pixels in a smoothed image patch.
- We then introduce a learning step to find less correlated binary tests leading to the better descriptor rBRIEF, for which we offer comparisons to SIFT and SURF.
- ORB is relatively immune to Gaussian image noise, unlike SIFT.
- In this section we show that ORB outperforms SIFT/SURF in nearest-neighbor matching over large databases of images.
- Detector ORB SURF SIFT Time per frame (ms) 15.
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