Machine Vision and Applications

, Volume 22, Issue 1, pp 207–217

Feature tracking and matching in video using programmable graphics hardware

  • Sudipta N. Sinha
  • Jan-Michael Frahm
  • Marc Pollefeys
  • Yakup Genc
Original Paper

DOI: 10.1007/s00138-007-0105-z

Cite this article as:
Sinha, S.N., Frahm, J., Pollefeys, M. et al. Machine Vision and Applications (2011) 22: 207. doi:10.1007/s00138-007-0105-z

Abstract

This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware, the CPU is freed up to run other computations in parallel. Our GPU-based KLT implementation tracks about a thousand features in real-time at 30 Hz on 1,024 × 768 resolution video which is a 20 times improvement over the CPU. The GPU-based SIFT implementation extracts about 800 features from 640 × 480 video at 10 Hz which is approximately 10 times faster than an optimized CPU implementation.

Keywords

Visual trackingVehicle trackingVideo surveillanceVisual inspectionVision systemRobot navigation

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Sudipta N. Sinha
    • 1
  • Jan-Michael Frahm
    • 1
  • Marc Pollefeys
    • 1
  • Yakup Genc
    • 2
  1. 1.Department of Computer Science, CB# 3175 Sitterson HallUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Real-time Vision and Modeling DepartmentSiemens Corporate ResearchPrincetonUSA