ORB in 5 ms: An Efficient SIMD Friendly Implementation

Abstract

AbstractOne of the key challenges today in computer vision applications is to be able to reliably detect features in real-time. The most prominent feature extraction methods are Speeded up Robust Features(SURF), Scale Invariant Feature Transform(SIFT) and Oriented FAST and Rotated BRIEF(ORB), which have proved to yield reliable features for applications such as object recognition and tracking. In this paper, we propose an efficient single instruction multiple data(SIMD) friendly implementation of ORB. This solution shows that ORB feature extraction can be effectively implemented in about 5.5 ms on a Vector SIMD engine such as Embedded Vision Engine(EVE) of Texas Instruments(TI). We also show that our implementation is reliable with the help of repeatability test.KeywordsScale Invariant Feature TransformCenter PixelMemory BandwidthCircular RingSingle Instruction Multiple DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.