IDENTIFICATION AND RECONSTRUCTION OF COMPLETE GAIT CYCLES FOR PERSON IDENTIFICATION IN CROWDED SCENES

Martin Hofmann, Daniel Wolf, Gerhard Rigoll

Abstract

This paper addresses the problem of gait recognition in the presence of occlusions. Recognition of people using their gait has been an active research area and many successful algorithms have been presented. However to this point non of the methods addresses the problem of occlusion. Most of the current algorithms need a full gait cycle for recognition. In this paper we present a scheme for reconstruction of full gait cycles, which can be used as preprocessing step for any state-of-the-art gait recognition method. We test this on the TUM-IITKGP gait recognition database and show a significant performance gain in the case of occlusions.

References

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Paper Citation


in Harvard Style

Hofmann M., Wolf D. and Rigoll G. (2011). IDENTIFICATION AND RECONSTRUCTION OF COMPLETE GAIT CYCLES FOR PERSON IDENTIFICATION IN CROWDED SCENES . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 594-597. DOI: 10.5220/0003329305940597


in Bibtex Style

@conference{visapp11,
author={Martin Hofmann and Daniel Wolf and Gerhard Rigoll},
title={IDENTIFICATION AND RECONSTRUCTION OF COMPLETE GAIT CYCLES FOR PERSON IDENTIFICATION IN CROWDED SCENES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={594-597},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003329305940597},
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 - IDENTIFICATION AND RECONSTRUCTION OF COMPLETE GAIT CYCLES FOR PERSON IDENTIFICATION IN CROWDED SCENES
SN - 978-989-8425-47-8
AU - Hofmann M.
AU - Wolf D.
AU - Rigoll G.
PY - 2011
SP - 594
EP - 597
DO - 10.5220/0003329305940597