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Foreground Detection Based on Superpixel and Semantic Segmentation

Foreground detection is an essential step in computer vision and video processing. Accurate foreground object extraction is crucial for subsequent high-level tasks such as target recognition and tracking. Although many foreground detection algorithms have been proposed, foreground detection in compl...

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Detalles Bibliográficos
Autores principales: Feng, Junying, Liu, Peng, Kim, Yong Kwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452948/
https://www.ncbi.nlm.nih.gov/pubmed/36093472
http://dx.doi.org/10.1155/2022/4331351
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author Feng, Junying
Liu, Peng
Kim, Yong Kwan
author_facet Feng, Junying
Liu, Peng
Kim, Yong Kwan
author_sort Feng, Junying
collection PubMed
description Foreground detection is an essential step in computer vision and video processing. Accurate foreground object extraction is crucial for subsequent high-level tasks such as target recognition and tracking. Although many foreground detection algorithms have been proposed, foreground detection in complex scenes is still a challenging problem. This paper presents a foreground detection algorithm based on superpixel and semantic segmentation. It first uses multiscale superpixel segmentation to obtain the initial foreground mask. At the same time, a semantic segmentation network is applied to separate potential foreground objects, and then use the defined rules to combine the results of superpixel and semantic segmentation to get the final foreground object. Finally, the background model is updated with the refined foreground result. Experiments on the CDNet2014 dataset demonstrate the effectiveness of the proposed algorithm, which can accurately segment foreground objects in complex scenes.
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spelling pubmed-94529482022-09-09 Foreground Detection Based on Superpixel and Semantic Segmentation Feng, Junying Liu, Peng Kim, Yong Kwan Comput Intell Neurosci Research Article Foreground detection is an essential step in computer vision and video processing. Accurate foreground object extraction is crucial for subsequent high-level tasks such as target recognition and tracking. Although many foreground detection algorithms have been proposed, foreground detection in complex scenes is still a challenging problem. This paper presents a foreground detection algorithm based on superpixel and semantic segmentation. It first uses multiscale superpixel segmentation to obtain the initial foreground mask. At the same time, a semantic segmentation network is applied to separate potential foreground objects, and then use the defined rules to combine the results of superpixel and semantic segmentation to get the final foreground object. Finally, the background model is updated with the refined foreground result. Experiments on the CDNet2014 dataset demonstrate the effectiveness of the proposed algorithm, which can accurately segment foreground objects in complex scenes. Hindawi 2022-08-31 /pmc/articles/PMC9452948/ /pubmed/36093472 http://dx.doi.org/10.1155/2022/4331351 Text en Copyright © 2022 Junying Feng 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
Feng, Junying
Liu, Peng
Kim, Yong Kwan
Foreground Detection Based on Superpixel and Semantic Segmentation
title Foreground Detection Based on Superpixel and Semantic Segmentation
title_full Foreground Detection Based on Superpixel and Semantic Segmentation
title_fullStr Foreground Detection Based on Superpixel and Semantic Segmentation
title_full_unstemmed Foreground Detection Based on Superpixel and Semantic Segmentation
title_short Foreground Detection Based on Superpixel and Semantic Segmentation
title_sort foreground detection based on superpixel and semantic segmentation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452948/
https://www.ncbi.nlm.nih.gov/pubmed/36093472
http://dx.doi.org/10.1155/2022/4331351
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