Computer Science ›› 2020, Vol. 47 ›› Issue (8): 105-111.doi: 10.11896/jsjkx.190700036

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Real-time SIFT Algorithm Based on GPU

WANG Liang, ZHOU Xin-zhi, YNA Hua   

  1. School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
  • Online:2020-08-15 Published:2020-08-10
  • About author:WANG Liang, born in 1993, postgra-duate.His main research interests include computer vision and parallel computing.
    YAN Hua, born in 1971, Ph.D, professor.His main research interests include intelligent algorithm, storage system and path planning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61403265).

Abstract: Aiming at the complex and low real-time defects of the SIFT feature extraction algorithm, a real-time SIFT algorithm based on GPU is proposed, called CoSift (CUDA Optimized SIFT).Firstly, the algorithm uses the CUDA stream concurrency mechanism to construct the SIFT scale space.In this process, the high-speed memory in the CUDA memory model is fully utilized to improve data access speed, and the two-dimensional Gaussian convolution kernel is optimized to reduce the amount of computation.Then, the warp-based histogram policy is designed to rebalance the workload during the characterization process.Compared with the traditional algorithm of the CPU version and the improved algorithm of the GPU version, the proposed algorithm greatly improves the real-time performance of the SIFT algorithm without reducing the accuracy of feature extraction, and has a relatively higher optimization effect on large-size images.CoSift can extract features within 7.7~8.8ms (116.28~129.87fps) on the GTX1080Ti.The algorithm effectively reduces the complexity of the traditional SIFT algorithm process, improves the real-time performance, and is convenient to be applied in scenarios where the real-time requirement of SIFT algorithm is higher.

Key words: Scale invariant feature transformation, Feature extraction, Real-time, CUDA, Parallel acceleration

CLC Number: 

  • TP391.4
[1] LOWE D G.Distinctive Image Features from Scale-InvariantKeypoints[J].International Journal of Computer Vision, 2004, 60(2):91-110.
[2] KE N Y, SUKTHANKAR R.PCA-SIFT:A More Distinctive Representation for Local Image Descriptors[C]∥Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.IEEE Computer Society, 2004.
[3] BAY H, TUYTELAARS T, GOOL L V.SURF:Speeded UpRobust Features[C]∥European Conference on Computer Vision.2006.
[4] DU C, YUAN J, DONG J, et al.GPU based Parallel Optimization for Real Time Panoramic Video Stitching[J].Pattern Recognition Letters, 2018, 133(5):62-69.
[5] ACHARYA K A, BABU R V, VADHIYAR S S.A Real-Time Implementation of SIFT Using GPU[J].Journal of Real-Time Image Processing, 2014, 14(8):267-277.
[6] ZHOU Y, MEI K, XIANG J, et al.Parallelization and Optimization of SIFT on GPU Using CUDA[C]∥IEEE International Conference on High Performance Computing & Communications & IEEE International Conference on Embedded & Ubiquitous Computing.2014.
[7] LI Z, JIA H, ZHANG Y.HartSift:A High-Accuracy and Real-Time SIFT Based on GPU[C]∥2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS).IEEE Computer Society, 2017.
[8] NVIDIA Corporation.CUDA Programming Guide 9.0[OL].https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html.
[9] NVIDIA Corporation.CUDA Toolkit Documentation v9.0[OL].https://docs.nvidia.com/cuda/cuda-c-programming-guide.
[10] TIAN W, XU F, WANG H Y, et al. Fast Scale Invariant Feature Transform Algorithm Based on CUDA.Computer Engineering, 2010, 36(8):219-221.
[11] YAN J H, HANG Y Q, XU J F, et al.Quick Realization of CUDA-Based Registration of High-Resolution Digital Video Images[J].Chinese Journal of Scientific Instrument, 2014, 35(2):380-386.
[12] RAGHU R P K, SURESH M, JOHN M.An Approach to Parallelization of SIFT Algorithm on GPUs for Real-Time Applications[J].Journal of Computer and Communications, 2016, 4(17):18-50.
[13] JIANG C, GENG Z X, LOU B, et al.Parallel Processing Re-search on SIFT Feature Matching Algorithm Based on GPU[J].Computer Science, 2013, 40(12):295-297, 307.
[14] WU C.SiftGPU :A GPU Implementation of Scale InvariantFeature Transform (SIFT)[OL].http://cs.unc.edu/~ccwu/siftgpu.
[15] BJRKMAN M, BERGSTRM N, KRAGIC D.Detecting, Segmenting and Tracking Unknown Objects Using Multi-label MRF Inference[J].Computer Vision and Image Understanding, 2014, 118:111-127.
[16] ZHANG K, YANG H Y, SHI L Y.Panorama Generation ofSIFT and Stitch Line Based on CUDA[J].Computer Technology and Development, 2015(9):22-26.
[17] ZHI X, YAN J, HANG Y, et al.Realization of CUDA-BasedReal-Time Registration and Target Localization for High-Resolution Video Images[J].Journal of Real-Time Image Proces-sing, 2016, 16:1025-1036.
[18] The Oxford Buildings Dataset[OL].http://www.robots.ox.ac.uk/~vgg/data/oxbuilding.
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