ML Anthology

On Fast Trackers That Are Robust to Partial Occlusions

Lu Zhang, Hamdi Dibeklioglu, Laurens van der Maaten
CVPRW 2014 pp. 718-719
doi:10.1109/CVPRW.2014.111 /cvprw/2014/zhang2014cvprw-fast/

Abstract

Model-free tracking aims identify the location of particular objects or object parts in each frame of a video based on a single positive example. In our work, we (1) develop online-learning algorithms for part-based models that facilitate the use of these models in model-free tracking in order to improve robustness to partial occlusions, and (2) derive a probabilistic bound that facilitates rapid pruning of candidate locations in many popular trackers. Together with other recent advances in object detection and tracking, we believe these developments will ultimately contribute to solving the long-term tracking problem.

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Cite

Text

Zhang et al. "On Fast Trackers That Are Robust to Partial Occlusions." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014. doi:10.1109/CVPRW.2014.111

Markdown

[Zhang et al. "On Fast Trackers That Are Robust to Partial Occlusions." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014.](https://mlanthology.org/cvprw/2014/zhang2014cvprw-fast/) doi:10.1109/CVPRW.2014.111

BibTeX

@inproceedings{zhang2014cvprw-fast,
  title     = {{On Fast Trackers That Are Robust to Partial Occlusions}},
  author    = {Zhang, Lu and Dibeklioglu, Hamdi and van der Maaten, Laurens},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2014},
  pages     = {718-719},
  doi       = {10.1109/CVPRW.2014.111},
  url       = {https://mlanthology.org/cvprw/2014/zhang2014cvprw-fast/}
}