Manifold learning-based clustering approach applied to anomaly detection in surveillance videos

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

Manifold learning-based clustering approach applied to anomaly detection in surveillance videos

Data

2020-01-01

Autores

Lopes, Leonardo Tadeu [UNESP]

Valem, Lucas Pascotti [UNESP]

Guimarães Pedronette, Daniel Carlos [UNESP]

Guilherme, Ivan Rizzo [UNESP]

Papa, João Paulo [UNESP]

Silva Santana, Marcos Cleison [UNESP]

Colombo, Danilo

Resumo

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.

Palavras-chave

Anomaly Detection, Clustering, Unsupervised Manifold Learning, Video Surveillance

Como citar

VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, v. 5, p. 404-412.