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

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Author(s):
Lopes, Leonardo Tadeu ; Valem, Lucas Pascotti ; Guimaraes Pedronette, Daniel Carlos ; Guilherme, Ivan Rizzo ; Papa, Joao Paulo ; Silva Santana, Marcos Cleison ; Colombo, Danilo ; Farinella, GM ; Radeva, P ; Braz, J
Total Authors: 10
Document type: Journal article
Source: VISAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4: VISAPP; v. N/A, p. 9-pg., 2020-01-01.
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. (AU)

FAPESP's process: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support Opportunities: Research Grants - Young Investigators Grants - Phase 2
FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert
Grantee:João Paulo Papa
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 19/07825-1 - Deep Boltzmann machines for event recognition in videos
Grantee:Mateus Roder
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 18/21934-5 - Network statistics: theory, methods, and applications
Grantee:André Fujita
Support Opportunities: Research Projects - Thematic Grants