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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A Novel Siamese-Based Approach for Scene Change Detection With Applications to Obstructed Routes in Hazardous Environments

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Author(s):
Santana, Marcos C. S. [1] ; Passos, Jr., Leandro Aparecido [1] ; Moreira, Thierry P. [1] ; Colombo, Danilo [2] ; de Albuquerque, Victor Hugo C. [3] ; Papa, Joao Paulo [1]
Total Authors: 6
Affiliation:
[1] Sao Paulo State Univ, Sao Paulo - Brazil
[2] Petr Brasileiro SA Petrobras, Rio De Janeiro - Brazil
[3] Univ Fortaleza, UNIFOR, Fortaleza, Ceara - Brazil
Total Affiliations: 3
Document type: Journal article
Source: IEEE INTELLIGENT SYSTEMS; v. 35, n. 1, p. 44-53, JAN-FEB 2020.
Web of Science Citations: 0
Abstract

The demand for automatic scene change detection has massively increased in the last decades due to its importance regarding safety and security issues. Although deep learning techniques have provided significant enhancements in the field, such methods must learn which object belongs to the foreground or background beforehand. In this article, we propose an approach that employs siamese U-Nets to address the task of change detection, such that the model learns to perform semantic segmentation using background reference frames only. Therefore, any object that comes up into the scene defines a change. The experimental results show the robustness of the proposed model over the well-known public dataset CDNet2014. Additionally, we also consider a private dataset called ``PetrobrasROUTES,{''} which comprises obstruction or abandoned objects in escape routes in hazardous environments. Moreover, the experiments show that the proposed approach is more robust to noise and illumination changes. (AU)

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 type: Research Projects - Thematic Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: 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 type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support type: Regular Research Grants