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Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture

Foreground detection is a classic video processing task, widely used in video surveillance and other fields, and is the basic step of many computer vision tasks. The scene in the real world is complex and changeable, and it is difficult for traditional unsupervised methods to accurately extract fore...

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Detalles Bibliográficos
Autores principales: Liu, Peng, Feng, Junying, Sang, Jianli, Kim, Yong Kwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512601/
https://www.ncbi.nlm.nih.gov/pubmed/36172321
http://dx.doi.org/10.1155/2022/7432615
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author Liu, Peng
Feng, Junying
Sang, Jianli
Kim, Yong Kwan
author_facet Liu, Peng
Feng, Junying
Sang, Jianli
Kim, Yong Kwan
author_sort Liu, Peng
collection PubMed
description Foreground detection is a classic video processing task, widely used in video surveillance and other fields, and is the basic step of many computer vision tasks. The scene in the real world is complex and changeable, and it is difficult for traditional unsupervised methods to accurately extract foreground targets. Based on deep learning theory, this paper proposes a foreground detection method based on the multiscale U-Net architecture with a fusion attention mechanism. The attention mechanism is introduced into the U-Net multiscale architecture through skip connections, causing the network model to pay more attention to the foreground objects, suppressing irrelevant background regions, and improving the learning ability of the model. We conducted experiments and evaluations on the CDnet-2014 dataset. The proposed model inputs a single RGB image and only utilizes spatial information, with an overall F-measure of 0.9785. The input of multiple images is fused, and the overall F-measure can reach 0.9830 by using spatiotemporal information. Especially in the Low Framerate category, the F-measure exceeds the current state-of-the-art methods. The experimental results demonstrate the effectiveness and superiority of our proposed method.
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spelling pubmed-95126012022-09-27 Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture Liu, Peng Feng, Junying Sang, Jianli Kim, Yong Kwan Comput Intell Neurosci Research Article Foreground detection is a classic video processing task, widely used in video surveillance and other fields, and is the basic step of many computer vision tasks. The scene in the real world is complex and changeable, and it is difficult for traditional unsupervised methods to accurately extract foreground targets. Based on deep learning theory, this paper proposes a foreground detection method based on the multiscale U-Net architecture with a fusion attention mechanism. The attention mechanism is introduced into the U-Net multiscale architecture through skip connections, causing the network model to pay more attention to the foreground objects, suppressing irrelevant background regions, and improving the learning ability of the model. We conducted experiments and evaluations on the CDnet-2014 dataset. The proposed model inputs a single RGB image and only utilizes spatial information, with an overall F-measure of 0.9785. The input of multiple images is fused, and the overall F-measure can reach 0.9830 by using spatiotemporal information. Especially in the Low Framerate category, the F-measure exceeds the current state-of-the-art methods. The experimental results demonstrate the effectiveness and superiority of our proposed method. Hindawi 2022-09-19 /pmc/articles/PMC9512601/ /pubmed/36172321 http://dx.doi.org/10.1155/2022/7432615 Text en Copyright © 2022 Peng Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Peng
Feng, Junying
Sang, Jianli
Kim, Yong Kwan
Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture
title Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture
title_full Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture
title_fullStr Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture
title_full_unstemmed Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture
title_short Fusion Attention Mechanism for Foreground Detection Based on Multiscale U-Net Architecture
title_sort fusion attention mechanism for foreground detection based on multiscale u-net architecture
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512601/
https://www.ncbi.nlm.nih.gov/pubmed/36172321
http://dx.doi.org/10.1155/2022/7432615
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