Multi‐label learning based target detecting from multi‐frame data

Abstract In the field of target detecting, lots of progress have been made in recent years. Owing to the progress of multiple frames time series data, or video satellites, target detecting from space‐borne satellite videos has been available. However, detecting a slightly moving target from space‐bo...

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Autores principales:	Mengqing Mei, Fazhi He
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Photography TR1-1050 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/b951507c7c1c4b27a8de2628cddefc8a
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id	oai:doaj.org-article:b951507c7c1c4b27a8de2628cddefc8a
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spelling	oai:doaj.org-article:b951507c7c1c4b27a8de2628cddefc8a2021-11-29T03:38:16ZMulti‐label learning based target detecting from multi‐frame data1751-96671751-965910.1049/ipr2.12271https://doaj.org/article/b951507c7c1c4b27a8de2628cddefc8a2021-12-01T00:00:00Zhttps://doi.org/10.1049/ipr2.12271https://doaj.org/toc/1751-9659https://doaj.org/toc/1751-9667Abstract In the field of target detecting, lots of progress have been made in recent years. Owing to the progress of multiple frames time series data, or video satellites, target detecting from space‐borne satellite videos has been available. However, detecting a slightly moving target from space‐borne videos is still a difficult task, because of the low resolution and illumination variation influence. This paper considers target detecting from time series data as multi‐label problem as there are several different kinds of background objects and targets of interest. To some extent the background of time series data is comparative invariant, using background analysis method to extract the target from the background is promising. This paper proposes a novel target detecting algorithm based on multi‐label learning and Gaussian background description model aiming at extracting slowly moving target. To further enhance performances, multi‐frame fusion and post processing method was utilized to catch the slight difference due to movement. Experimental results on real world datasets indicate that the proposed method outperforms some state‐of‐the‐art algorithms.Mengqing MeiFazhi HeWileyarticlePhotographyTR1-1050Computer softwareQA76.75-76.765ENIET Image Processing, Vol 15, Iss 14, Pp 3638-3644 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Photography TR1-1050 Computer software QA76.75-76.765
spellingShingle	Photography TR1-1050 Computer software QA76.75-76.765 Mengqing Mei Fazhi He Multi‐label learning based target detecting from multi‐frame data
description	Abstract In the field of target detecting, lots of progress have been made in recent years. Owing to the progress of multiple frames time series data, or video satellites, target detecting from space‐borne satellite videos has been available. However, detecting a slightly moving target from space‐borne videos is still a difficult task, because of the low resolution and illumination variation influence. This paper considers target detecting from time series data as multi‐label problem as there are several different kinds of background objects and targets of interest. To some extent the background of time series data is comparative invariant, using background analysis method to extract the target from the background is promising. This paper proposes a novel target detecting algorithm based on multi‐label learning and Gaussian background description model aiming at extracting slowly moving target. To further enhance performances, multi‐frame fusion and post processing method was utilized to catch the slight difference due to movement. Experimental results on real world datasets indicate that the proposed method outperforms some state‐of‐the‐art algorithms.
format	article
author	Mengqing Mei Fazhi He
author_facet	Mengqing Mei Fazhi He
author_sort	Mengqing Mei
title	Multi‐label learning based target detecting from multi‐frame data
title_short	Multi‐label learning based target detecting from multi‐frame data
title_full	Multi‐label learning based target detecting from multi‐frame data
title_fullStr	Multi‐label learning based target detecting from multi‐frame data
title_full_unstemmed	Multi‐label learning based target detecting from multi‐frame data
title_sort	multi‐label learning based target detecting from multi‐frame data
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/b951507c7c1c4b27a8de2628cddefc8a
work_keys_str_mv	AT mengqingmei multilabellearningbasedtargetdetectingfrommultiframedata AT fazhihe multilabellearningbasedtargetdetectingfrommultiframedata
_version_	1718407668957184000

Multi‐label learning based target detecting from multi‐frame data

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