| Full text | |
| 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 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: | 16/19403-6 - Energy-based learning models and their applications |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |