Learning and Classification of Car Trajectories in Road Video by String Kernels

Luc Brun, Alessia Saggese, Mario Vento

2013

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.

Paper Citation

in Harvard Style

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 - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 709-714. DOI: 10.5220/0004301207090714

in Bibtex Style

@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 - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={709-714},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004301207090714},
isbn={978-989-8565-47-1},
}

in EndNote Style

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