DYNAMIC GLOBAL OPTIMIZATION FRAMEWORK FOR REAL-TIME TRACKING
João F. Henriques, Rui Caseiro, Jorge Batista
2010
Abstract
Tracking is a crucial task in the context of visual surveillance. There are roughly three classes of trackers: the classical greedy algorithms (based on sequential modeling of targets, such as particle filters), Multiple Hypothesis Tracking (MHT) and its variants, and global optimizers (based on optimal matching algorithms from linear programming). We point out the shortcomings of all approaches, and set out to solve the only gaping deficiency of global optimization trackers, which is their inability to work with streamed video, in continual operation. We present an extension to the new Dynamic Hungarian Algorithm that achieves this effect, and show tracking results in such different conditions as the tracking of humans and vehicles, in different scenes, using the same set of parameters for our tracker.
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Paper Citation
in Harvard Style
Henriques J., Caseiro R. and Batista J. (2010). DYNAMIC GLOBAL OPTIMIZATION FRAMEWORK FOR REAL-TIME TRACKING . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 207-215. DOI: 10.5220/0002823502070215
in Bibtex Style
@conference{visapp10,
author={João F. Henriques and Rui Caseiro and Jorge Batista},
title={DYNAMIC GLOBAL OPTIMIZATION FRAMEWORK FOR REAL-TIME TRACKING},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={207-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002823502070215},
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 - DYNAMIC GLOBAL OPTIMIZATION FRAMEWORK FOR REAL-TIME TRACKING
SN - 978-989-674-028-3
AU - Henriques J.
AU - Caseiro R.
AU - Batista J.
PY - 2010
SP - 207
EP - 215
DO - 10.5220/0002823502070215