ML Anthology

Linear Predictors for Fast Simultaneous Modeling and Tracking

Liam F. Ellis, Nicholas D. H. Dowson, Jiri Matas, Richard Bowden
ICCV 2007 pp. 1-8
doi:10.1109/ICCV.2007.4409187 /iccv/2007/ellis2007iccv-linear/

Abstract

An approach for fast tracking of arbitrary image features with no prior model and no offline learning stage is presented. Fast tracking is achieved using banks of linear displacement predictors learnt online. A multi-modal appearance model is also learnt on-the-fly that facilitates the selection of subsets of predictors suitable for prediction in the next frame. The approach is demonstrated in real-time on a number of challenging video sequences and experimentally compared to other simultaneous modeling and tracking approaches with favourable results.

PDF ICCV Semantic Scholar

Cite

Text

Ellis et al. "Linear Predictors for Fast Simultaneous Modeling and Tracking." IEEE/CVF International Conference on Computer Vision, 2007. doi:10.1109/ICCV.2007.4409187

Markdown

[Ellis et al. "Linear Predictors for Fast Simultaneous Modeling and Tracking." IEEE/CVF International Conference on Computer Vision, 2007.](https://mlanthology.org/iccv/2007/ellis2007iccv-linear/) doi:10.1109/ICCV.2007.4409187

BibTeX

@inproceedings{ellis2007iccv-linear,
  title     = {{Linear Predictors for Fast Simultaneous Modeling and Tracking}},
  author    = {Ellis, Liam F. and Dowson, Nicholas D. H. and Matas, Jiri and Bowden, Richard},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2007},
  pages     = {1-8},
  doi       = {10.1109/ICCV.2007.4409187},
  url       = {https://mlanthology.org/iccv/2007/ellis2007iccv-linear/}
}