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Authors: Luc Brun 1 ; Pasquale Foggia 2 ; Alessia Saggese 2 and Mario Vento 2

Affiliations: 1 ENSICAEN - Université de Caen Basse-Normandie, France ; 2 University of Salerno, Italy

Keyword(s): Action Recognition, Human Behavior Analysis, String Based Representations, Edit Distance.

Abstract: In this paper we propose a novel method for human action recognition based on string edit distance. A two layer representation is introduced in order to exploit the temporal sequence of the events: a first representation layer is obtained by using a feature vector obtained from depth images. Then, each action is represented as a sequence of symbols, where each symbol corresponding to an elementary action (aclet) is obtained according to a dictionary previously defined during the learning phase. The similarity between two actions is finally computed in terms of string edit distance, which allows the system to deal with actions showing different length as well as different temporal scales. The experimentation has been carried out on two widely adopted datasets, namely the MIVIA and the MHAD datasets, and the obtained results, compared with state of the art approaches, confirm the effectiveness of the proposed method.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Brun, L.; Foggia, P.; Saggese, A. and Vento, M. (2015). Recognition of Human Actions using Edit Distance on Aclet Strings. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP; ISBN 978-989-758-090-1; ISSN 2184-4321, SciTePress, pages 97-103. DOI: 10.5220/0005304700970103

@conference{visapp15,
author={Luc Brun. and Pasquale Foggia. and Alessia Saggese. and Mario Vento.},
title={Recognition of Human Actions using Edit Distance on Aclet Strings},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP},
year={2015},
pages={97-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005304700970103},
isbn={978-989-758-090-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP
TI - Recognition of Human Actions using Edit Distance on Aclet Strings
SN - 978-989-758-090-1
IS - 2184-4321
AU - Brun, L.
AU - Foggia, P.
AU - Saggese, A.
AU - Vento, M.
PY - 2015
SP - 97
EP - 103
DO - 10.5220/0005304700970103
PB - SciTePress