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Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System
This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algo...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970033/ https://www.ncbi.nlm.nih.gov/pubmed/27347978 http://dx.doi.org/10.3390/s16070982 |
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author | Jung, Jaehoon Yoon, Inhye Paik, Joonki |
author_facet | Jung, Jaehoon Yoon, Inhye Paik, Joonki |
author_sort | Jung, Jaehoon |
collection | PubMed |
description | This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. |
format | Online Article Text |
id | pubmed-4970033 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-49700332016-08-04 Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System Jung, Jaehoon Yoon, Inhye Paik, Joonki Sensors (Basel) Article This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. MDPI 2016-06-25 /pmc/articles/PMC4970033/ /pubmed/27347978 http://dx.doi.org/10.3390/s16070982 Text en © 2016 by the authors; 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jung, Jaehoon Yoon, Inhye Paik, Joonki Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System |
title | Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System |
title_full | Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System |
title_fullStr | Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System |
title_full_unstemmed | Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System |
title_short | Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System |
title_sort | object occlusion detection using automatic camera calibration for a wide-area video surveillance system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970033/ https://www.ncbi.nlm.nih.gov/pubmed/27347978 http://dx.doi.org/10.3390/s16070982 |
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