3886

Fast gain-adaptive KLT tracking on the GPU

Christopher Zach, David Gallup, Jan-Michael Frahm
University of North Carolina, Chapel Hill, NC
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008. CVPRW ’08

@inproceedings{zach2008fast,

   title={Fast gain-adaptive KLT tracking on the GPU},

   author={Zach, C. and Gallup, D. and Frahm, J.M.},

   booktitle={Computer Vision and Pattern Recognition Workshops, 2008. CVPRW’08. IEEE Computer Society Conference on},

   pages={1–7},

   year={2008},

   organization={IEEE}

}

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 times 576) resolution data, and delivers real-time performance even on low-end mobile graphics processors.
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