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Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios
Moving target detection (MTD) is a crucial task in computer vision applications. In this paper, we investigate the problem of detecting moving targets in infrared (IR) surveillance video sequences captured using a steady camera in a maritime setting. For this purpose, we employ robust principal comp...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10384380/ https://www.ncbi.nlm.nih.gov/pubmed/37514626 http://dx.doi.org/10.3390/s23146334 |
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author | Pulpito, Osvaldo Acito, Nicola Diani, Marco Ferri, Gabriele Grasso, Raffaele Zissis, Dimitris |
author_facet | Pulpito, Osvaldo Acito, Nicola Diani, Marco Ferri, Gabriele Grasso, Raffaele Zissis, Dimitris |
author_sort | Pulpito, Osvaldo |
collection | PubMed |
description | Moving target detection (MTD) is a crucial task in computer vision applications. In this paper, we investigate the problem of detecting moving targets in infrared (IR) surveillance video sequences captured using a steady camera in a maritime setting. For this purpose, we employ robust principal component analysis (RPCA), which is an improvement of principal component analysis (PCA) that separates an input matrix into the following two matrices: a low-rank matrix that is representative, in our case study, of the slowly changing background, and a sparse matrix that is representative of the foreground. RPCA is usually implemented in a non-causal batch form. To pursue a real-time application, we tested an online implementation, which, unfortunately, was affected by the presence of the target in the scene during the initialization phase. Therefore, we improved the robustness by implementing a saliency-based strategy. The advantages offered by the resulting technique, which we called “saliency-aided online moving window RPCA” (S-OMW-RPCA) are the following: RPCA is implemented online; along with the temporal features exploited by RPCA, the spatial features are also taken into consideration by using a saliency filter; the results are robust against the condition of the scene during the initialization. Finally, we compare the performance of the proposed technique in terms of precision, recall, and execution time with that of an online RPCA, thus, showing the effectiveness of the saliency-based approach. |
format | Online Article Text |
id | pubmed-10384380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103843802023-07-30 Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios Pulpito, Osvaldo Acito, Nicola Diani, Marco Ferri, Gabriele Grasso, Raffaele Zissis, Dimitris Sensors (Basel) Article Moving target detection (MTD) is a crucial task in computer vision applications. In this paper, we investigate the problem of detecting moving targets in infrared (IR) surveillance video sequences captured using a steady camera in a maritime setting. For this purpose, we employ robust principal component analysis (RPCA), which is an improvement of principal component analysis (PCA) that separates an input matrix into the following two matrices: a low-rank matrix that is representative, in our case study, of the slowly changing background, and a sparse matrix that is representative of the foreground. RPCA is usually implemented in a non-causal batch form. To pursue a real-time application, we tested an online implementation, which, unfortunately, was affected by the presence of the target in the scene during the initialization phase. Therefore, we improved the robustness by implementing a saliency-based strategy. The advantages offered by the resulting technique, which we called “saliency-aided online moving window RPCA” (S-OMW-RPCA) are the following: RPCA is implemented online; along with the temporal features exploited by RPCA, the spatial features are also taken into consideration by using a saliency filter; the results are robust against the condition of the scene during the initialization. Finally, we compare the performance of the proposed technique in terms of precision, recall, and execution time with that of an online RPCA, thus, showing the effectiveness of the saliency-based approach. MDPI 2023-07-12 /pmc/articles/PMC10384380/ /pubmed/37514626 http://dx.doi.org/10.3390/s23146334 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pulpito, Osvaldo Acito, Nicola Diani, Marco Ferri, Gabriele Grasso, Raffaele Zissis, Dimitris Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios |
title | Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios |
title_full | Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios |
title_fullStr | Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios |
title_full_unstemmed | Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios |
title_short | Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios |
title_sort | saliency-aided online rpca for moving target detection in infrared maritime scenarios |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10384380/ https://www.ncbi.nlm.nih.gov/pubmed/37514626 http://dx.doi.org/10.3390/s23146334 |
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