The Community for Technology Leaders
2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2008)
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-7
Jan-Michael Frahm , University of North Carolina, Chapel Hill, USA
David Gallup , University of North Carolina, Chapel Hill, USA
Christopher Zach , University of North Carolina, Chapel Hill, USA
ABSTRACT
High-performance feature tracking from video input is a valuable tool in many computer vision techniques and mixed reality applications. This work presents a refined and substantially accelerated approach to KLT feature tracking performed on the GPU. Additionally, a global gain ratio between successive frames is estimated to compensate for changes in the camera exposure. The proposed approach achieves more than 200 frames per second on state-of-the art consumer GPUs for PAL (720 × 576) resolution data, and delivers real-time performance even on low-end mobile graphics processors.
INDEX TERMS
CITATION
Jan-Michael Frahm, David Gallup, Christopher Zach, "Fast gain-adaptive KLT tracking on the GPU", 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 00, no. , pp. 1-7, 2008, doi:10.1109/CVPRW.2008.4563089
94 ms
(Ver )