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

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

Keyword(s): Abnormal Trajectories Recognition, Abnormal Behaviors Recognition, String Kernel.

Abstract: An abnormal behavior of a moving vehicle or a moving person is characterized by an unusual or not expected trajectory. The definition of expected trajectories refers to supervised learning, where an human operator should define expected behaviors. Conversely, definition of usual trajectories, requires to learn automatically the dynamic of a scene in order to extract its typical trajectories. We propose, in this paper, a method able to identify abnormal behaviors based on a new unsupervised learning algorithm. The original contributions of the paper lies in the following aspects: first, the evaluation of similarities between trajectories is based on string kernels. Such kernels allow us to define a kernel-based clustering algorithm in order to obtain groups of similar trajectories. Finally, identification of abnormal trajectories is performed according to the typical trajectories characterized during the clustering step. The experimentation, conducted over a real dataset, confirms the efficiency of the proposed method. (More)

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Paper citation in several formats:
Brun, L.; Saggese, A. and Vento, M. (2013). Learning and Classification of Car Trajectories in Road Video by String Kernels. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP; ISBN 978-989-8565-47-1; ISSN 2184-4321, SciTePress, pages 709-714. DOI: 10.5220/0004301207090714

@conference{visapp13,
author={Luc Brun. and Alessia Saggese. and Mario Vento.},
title={Learning and Classification of Car Trajectories in Road Video by String Kernels},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP},
year={2013},
pages={709-714},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004301207090714},
isbn={978-989-8565-47-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP
TI - Learning and Classification of Car Trajectories in Road Video by String Kernels
SN - 978-989-8565-47-1
IS - 2184-4321
AU - Brun, L.
AU - Saggese, A.
AU - Vento, M.
PY - 2013
SP - 709
EP - 714
DO - 10.5220/0004301207090714
PB - SciTePress

- Science and Technology Publications, Lda.
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