Feature tracking and matching in video using programmable graphics hardware

Sudipta N. Sinha, Jan-Michael Frahm, Marc Pollefeys, Yakup Genc
2007 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
more » ... in real-time at 30 Hz on 1024 × 768 resolution video which is a 20 times improvement over the CPU. The GPU-based Send offprint requests to: Present address: Insert the address here if needed 2 Sudipta N. Sinha et al. SIFT implementation extracts about 800 features from 640 × 480 video at 10Hz which is approximately 10 times faster than an optimized CPU implementation.
doi:10.1007/s00138-007-0105-z fatcat:a5z6a3i2gvby7ayrha4t45zj3a