Manifold Learning-based Clustering Approach Applied to Anomaly Detection in Surveillance Videos

Leonardo Lopes, Lucas Valem, Daniel Pedronette, Ivan Guilherme, João Papa, Marcos Santana, Danilo Colombo

Abstract

The huge increase in the amount of multimedia data available and the pressing need for organizing them in different categories, especially in scenarios where there are no labels available, makes data clustering an essential task in different scenarios. In this work, we present a novel clustering method based on an unsupervised manifold learning algorithm, in which a more effective similarity measure is computed by the manifold learning and used for clustering purposes. The proposed approach is applied to anomaly detection in videos and used in combination with different background segmentation methods to improve their effectiveness. An experimental evaluation is conducted on three different image datasets and one video dataset. The obtained results indicate superior accuracy in most clustering tasks when compared to the baselines. Results also demonstrate that the clustering step can improve the results of background subtraction approaches in the majority of cases.

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Paper Citation


in Harvard Style

Lopes L., Valem L., Pedronette D., Guilherme I., Papa J., Santana M. and Colombo D. (2020). Manifold Learning-based Clustering Approach Applied to Anomaly Detection in Surveillance Videos.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2, pages 404-412. DOI: 10.5220/0008974604040412


in Bibtex Style

@conference{visapp20,
author={Leonardo Lopes and Lucas Valem and Daniel Pedronette and Ivan Guilherme and João Papa and Marcos Santana and Danilo Colombo},
title={Manifold Learning-based Clustering Approach Applied to Anomaly Detection in Surveillance Videos},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={404-412},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008974604040412},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Manifold Learning-based Clustering Approach Applied to Anomaly Detection in Surveillance Videos
SN - 978-989-758-402-2
AU - Lopes L.
AU - Valem L.
AU - Pedronette D.
AU - Guilherme I.
AU - Papa J.
AU - Santana M.
AU - Colombo D.
PY - 2020
SP - 404
EP - 412
DO - 10.5220/0008974604040412