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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Fast gain-adaptive KLT tracking on the GPU
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
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
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, pp.1-7, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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