ORB in 5 ms: An Efficient SIMD Friendly Implementation

  • Prashanth Viswanath
  • Pramod Swami
  • Kumar Desappan
  • Anshu Jain
  • Anoop Pathayapurakkal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9008)


One 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.


Scale Invariant Feature Transform Center Pixel Memory Bandwidth Circular Ring Single Instruction Multiple Data 
These 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.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Prashanth Viswanath
    • 1
  • Pramod Swami
    • 1
  • Kumar Desappan
    • 1
  • Anshu Jain
    • 1
  • Anoop Pathayapurakkal
    • 1
  1. 1.Texas Instruments India Private LtdBangaloreIndia

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