INCREMENTAL LEARNING AND VALIDATION OF SEQUENTIAL PREDICTORS IN VIDEO BROWSING APPLICATION

David Hurych, Tomáš Svoboda

2010

Abstract

Loss-of-track detection (tracking validation) and automatic tracker adaptation to new object appearances are attractive topics in computer vision. We apply very efficient learnable sequential predictors in order to address both issues. Validation is done by clustering of the sequential predictor responses. No aditional object model for validation is needed. The paper also proposes an incremental learning procedure that accommodates changing object appearance, which mainly improves the recall of the tracker/detector. Exemplars for the incremental learning are collected automatically, no user interaction is required. The aditional training examples are selected automatically using the tracker stability computed for each potential aditional training example. Coupled with a sparsely applied SIFT or SURF based detector the method is employed for object localization in videos. Our Matlab implementation scans videosequences up to eight times faster than the actual frame rate. A standard-length movie can be thus searched through in terms of minutes.

Paper Citation

in Harvard Style

Hurych D. and Svoboda T. (2010). INCREMENTAL LEARNING AND VALIDATION OF SEQUENTIAL PREDICTORS IN VIDEO BROWSING APPLICATION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 467-474. DOI: 10.5220/0002836504670474

in Bibtex Style

@conference{visapp10,
author={David Hurych and Tomáš Svoboda},
title={INCREMENTAL LEARNING AND VALIDATION OF SEQUENTIAL PREDICTORS IN VIDEO BROWSING APPLICATION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={467-474},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002836504670474},
isbn={978-989-674-028-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - INCREMENTAL LEARNING AND VALIDATION OF SEQUENTIAL PREDICTORS IN VIDEO BROWSING APPLICATION
SN - 978-989-674-028-3
AU - Hurych D.
AU - Svoboda T.
PY - 2010
SP - 467
EP - 474
DO - 10.5220/0002836504670474