Active Frame, Location, and Detector Selection for Automated and Manual Video Annotation

Vasiliy Karasev, Avinash Ravichandran, Stefano Soatto

CVPR 2014

doi:10.1109/CVPR.2014.273 /cvpr/2014/karasev2014cvpr-active/

Abstract

We describe an information-driven active selection approach to determine which detectors to deploy at which location in which frame of a video to minimize semantic class label uncertainty at every pixel, with the smallest computational cost that ensures a given uncertainty bound. We show minimal performance reduction compared to a "paragon" algorithm running all detectors at all locations in all frames, at a small fraction of the computational cost. Our method can handle uncertainty in the labeling mechanism, so it can handle both "oracles" (manual annotation) or noisy detectors (automated annotation).

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Cite

Text

Karasev et al. "Active Frame, Location, and Detector Selection for Automated and Manual Video Annotation." Conference on Computer Vision and Pattern Recognition, 2014. doi:10.1109/CVPR.2014.273

Markdown

[Karasev et al. "Active Frame, Location, and Detector Selection for Automated and Manual Video Annotation." Conference on Computer Vision and Pattern Recognition, 2014.](https://mlanthology.org/cvpr/2014/karasev2014cvpr-active/) doi:10.1109/CVPR.2014.273

BibTeX

@inproceedings{karasev2014cvpr-active,
  title     = {{Active Frame, Location, and Detector Selection for Automated and Manual Video Annotation}},
  author    = {Karasev, Vasiliy and Ravichandran, Avinash and Soatto, Stefano},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  doi       = {10.1109/CVPR.2014.273},
  url       = {https://mlanthology.org/cvpr/2014/karasev2014cvpr-active/}
}