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A real-time implementation of SIFT using GPU

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Abstract

Scale-Invariant Feature Transform (SIFT) is one of the widely used interest point features. It has been successfully applied in various computer vision algorithms like object detection, object tracking, robotic mapping and large-scale image retrieval. Although SIFT descriptors are highly robust towards scale and rotation variations, the high computational complexity of the SIFT algorithm inhibits its use in applications demanding real-time response, and in algorithms dealing with very large-scale databases. This paper presents a parallel implementation of SIFT on a GPU, where we obtain a speed of around 55 fps for a 640 × 480 image. One of the main contributions of our work is the novel combined kernel optimization that has led to a significant improvement of 12.2 % in the execution speed. We compare our results with the existing SIFT implementations in the literature, and find that our implementation has better speedup than most of them.

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Authors and Affiliations

  1. Video Analytics Laboratory, Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India

    K. Aniruddha Acharya & R. Venkatesh Babu

  2. Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, India

    Sathish S. Vadhiyar

Authors
  1. K. Aniruddha Acharya
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  2. R. Venkatesh Babu
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  3. Sathish S. Vadhiyar
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Correspondence to R. Venkatesh Babu.

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Acharya, K.A., Venkatesh Babu, R. & Vadhiyar, S.S. A real-time implementation of SIFT using GPU. J Real-Time Image Proc 14, 267–277 (2018). https://doi.org/10.1007/s11554-014-0446-6

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  • DOI: https://doi.org/10.1007/s11554-014-0446-6

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