MULTI-CAMERA PEOPLE TRACKING WITH HIERARCHICAL LIKELIHOOD GRIDS

Lili Chen, Giorgio Panin, Alois Knoll

Abstract

In this paper, we present a grid-based tracking by detection methodology, applied to 3D people tracking for multi-camera video surveillance. In particular, frame-by-frame detection is performed by means of hierarchical likelihood grids, using edge matching through the oriented distance transform on each camera view and a simple person model, followed by likelihood grids clustering in state-space. Subsequently, the tracking module performs a global nearest neighbor data association, in order to initiate, maintain and terminate tracks automatically. The proposed system can easily include additional features, such as color or background subtraction, it can be scaled to more camera views, and it can be used to track other items as well. We demonstrate it through experiments in indoor sequences, using a calibrated multi-camera setup.

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Paper Citation


in Harvard Style

Chen L., Panin G. and Knoll A. (2011). MULTI-CAMERA PEOPLE TRACKING WITH HIERARCHICAL LIKELIHOOD GRIDS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 474-483. DOI: 10.5220/0003316904740483


in Bibtex Style

@conference{visapp11,
author={Lili Chen and Giorgio Panin and Alois Knoll},
title={MULTI-CAMERA PEOPLE TRACKING WITH HIERARCHICAL LIKELIHOOD GRIDS},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={474-483},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003316904740483},
isbn={978-989-8425-47-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - MULTI-CAMERA PEOPLE TRACKING WITH HIERARCHICAL LIKELIHOOD GRIDS
SN - 978-989-8425-47-8
AU - Chen L.
AU - Panin G.
AU - Knoll A.
PY - 2011
SP - 474
EP - 483
DO - 10.5220/0003316904740483