ML Anthology

Fast Gain-Adaptive KLT Tracking on the GPU

Christopher Zach, David Gallup, Jan-Michael Frahm
CVPRW 2008 pp. 1-7
doi:10.1109/CVPRW.2008.4563089 /cvprw/2008/zach2008cvprw-fast/

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

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Cite

Text

Zach et al. "Fast Gain-Adaptive KLT Tracking on the GPU." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2008. doi:10.1109/CVPRW.2008.4563089

Markdown

[Zach et al. "Fast Gain-Adaptive KLT Tracking on the GPU." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2008.](https://mlanthology.org/cvprw/2008/zach2008cvprw-fast/) doi:10.1109/CVPRW.2008.4563089

BibTeX

@inproceedings{zach2008cvprw-fast,
  title     = {{Fast Gain-Adaptive KLT Tracking on the GPU}},
  author    = {Zach, Christopher and Gallup, David and Frahm, Jan-Michael},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2008},
  pages     = {1-7},
  doi       = {10.1109/CVPRW.2008.4563089},
  url       = {https://mlanthology.org/cvprw/2008/zach2008cvprw-fast/}
}