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

Leonardo Tadeu Lopes, Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette, Ivan Rizzo Guilherme, João Paulo Papa, Marcos Cleison Silva Santana, Danilo Colombo

2020

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.

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 (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 404-412. DOI: 10.5220/0008974604040412

in Bibtex Style

@conference{visapp20,
author={Leonardo Tadeu Lopes and Lucas Pascotti Valem and Daniel Carlos Guimarães Pedronette and Ivan Rizzo Guilherme and João Paulo Papa and Marcos Cleison Silva 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 (VISIGRAPP 2020) - 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 (VISIGRAPP 2020) - 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
PB - SciTePress