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

• Autor(a) principal: Lopes, Leonardo Tadeu [UNESP]
• Data de Publicação: 2020
• Outros Autores: Valem, Lucas Pascotti [UNESP], Guimaraes Pedronette, Daniel Carlos [UNESP], Guilherme, Ivan Rizzo [UNESP], Papa, Joao Paulo [UNESP], Silva Santana, Marcos Cleison [UNESP], Colombo, Danilo, Farinella, G. M., Radeva, P., Braz, J.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.5220/0008974604040412; http://hdl.handle.net/11449/209526
• topic: Clustering; Unsupervised Manifold Learning; Anomaly Detection; Video Surveillance

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.