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

Vasiliy Karasev, Avinash Ravichandran, Stefano Soatto; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 2123-2130

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).

Related Material


[pdf]
[bibtex]
@InProceedings{Karasev_2014_CVPR,
author = {Karasev, Vasiliy and Ravichandran, Avinash and Soatto, Stefano},
title = {Active Frame, Location, and Detector Selection for Automated and Manual Video Annotation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}