Anomaly Detection in Crowded Scenes Using Log-Euclidean Covariance Matrix

Efsun Sefa Sezer, Ahmet Burak Can

Abstract

In this paper, we propose an approach for anomaly detection in crowded scenes. For this purpose, two important types of features that encode motion and appearance cues are combined with the help of covariance matrix. Covariance matrices are symmetric positive definite (SPD) matrices which lie in the Riemannian manifold and are not suitable for Euclidean operations. To make covariance matrices suitable for use in the Euclidean space, they are converted to log-Euclidean covariance matrices (LECM) by using log-Euclidean framework. Then LECM features created in two different ways are used with one-class SVM to detect abnormal events. Experiments carried out on an anomaly detection benchmark dataset and comparison made with previous studies show that successful results are obtained.

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Paper Citation


in Harvard Style

Sezer E. and Can A. (2018). Anomaly Detection in Crowded Scenes Using Log-Euclidean Covariance Matrix.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-290-5, pages 279-286. DOI: 10.5220/0006618402790286


in Bibtex Style

@conference{visapp18,
author={Efsun Sefa Sezer and Ahmet Burak Can},
title={Anomaly Detection in Crowded Scenes Using Log-Euclidean Covariance Matrix},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2018},
pages={279-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006618402790286},
isbn={978-989-758-290-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Anomaly Detection in Crowded Scenes Using Log-Euclidean Covariance Matrix
SN - 978-989-758-290-5
AU - Sezer E.
AU - Can A.
PY - 2018
SP - 279
EP - 286
DO - 10.5220/0006618402790286