Feature tracking and matching in video using programmable graphics hardware

Sudipta Sinha, Jan-Michael Frahm, Marc Pollefeys, Yakup Genc
Department of Computer Science, CB# 3175 Sitterson Hall, University of North Carolina at Chapel Hill, NC 27599
Machine Vision and Applications


   title={Feature tracking and matching in video using programmable graphics hardware},

   author={Sinha, S.N. and Frahm, J.M. and Pollefeys, M. and Genc, Y.},

   journal={Machine Vision and Applications},






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 30Hz on 1024*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 10Hz which is approximately 10 times faster than an optimized CPU implementation.
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