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ORB in 5 ms: An Efficient SIMD Friendly Implementation

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9008))

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Abstract

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.

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Authors and Affiliations

  1. Texas Instruments India Private Ltd, Bangalore, Karnataka, India

    Prashanth Viswanath, Pramod Swami, Kumar Desappan, Anshu Jain & Anoop Pathayapurakkal

Authors
  1. Prashanth Viswanath
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  2. Pramod Swami
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  3. Kumar Desappan
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  4. Anshu Jain
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  5. Anoop Pathayapurakkal
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Corresponding author

Correspondence to Prashanth Viswanath .

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institue of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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© 2015 Springer International Publishing Switzerland

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Viswanath, P., Swami, P., Desappan, K., Jain, A., Pathayapurakkal, A. (2015). ORB in 5 ms: An Efficient SIMD Friendly Implementation. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_48

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16627-8

  • Online ISBN: 978-3-319-16628-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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