The Community for Technology Leaders
RSS Icon
Subscribe
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-7
Christopher Zach , University of North Carolina, Chapel Hill, USA
David Gallup , University of North Carolina, Chapel Hill, USA
Jan-Michael Frahm , 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.
CITATION
Christopher Zach, David Gallup, Jan-Michael Frahm, "Fast gain-adaptive KLT tracking on the GPU", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-7, doi:10.1109/CVPRW.2008.4563089
30 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool