MOD-IR: moving objects detection from UAV-captured video sequences based on image registration.
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| Title: | MOD-IR: moving objects detection from UAV-captured video sequences based on image registration. |
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| Authors: | Bouhlel, Fatma1 (AUTHOR) fatma.bouhlel@fss.usf.tn, Mliki, Hazar2,3 (AUTHOR), Hammami, Mohamed1 (AUTHOR) |
| Source: | Multimedia Tools & Applications. May2024, Vol. 83 Issue 16, p46779-46798. 20p. |
| Subjects: | United States. Defense Advanced Research Projects Agency, Image registration, Feature extraction, Drone aircraft |
| Abstract: | The moving objects detection from freely moving camera like the one mounted on Unmanned Aerial Vehicle (UAV) stands as an important and challenging issue. This paper introduced a new MOD-IR method for moving objects detection from UAV-captured video sequences. The proposed method consists of four steps: (1) feature extraction and matching, (2) frame registration, (3) moving objects detection and (4) moving objects detection post-processing. Our method stands out from those of the literature in a number of ways. First, we enhanced the method effectiveness and robustness by handling the constraints related to this field through extracting robust features, on the one hand, and automatically defining the optimum threshold, on the other. Second, we proposed an efficient method able to deal with real-time applications by extracting keypoint features instead of pixel-to-pixel model estimation, and by simulating the search for the matching features among multiple trees. Finally, we involved the quick-shift segmentation in parallel with the three first steps, in order to enhance and accelerate the moving objects detection task. Relying on quantitative and qualitative evaluations of the proposed method on a variety of sequences extracted from several datasets (such as DARPA VIVID-EgTest05, Hopkins 155, UCF Aerial Action, etc.), we assessed the performance of our method compared to the state-of-the-art reference methods. Furthermore, the time cost evaluation has enabled us to emphasize that our MOD-IR method is the optimal choice for real-time applications, owing to its lower computational time requirement compared to the reference methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 177079280 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: MOD-IR: moving objects detection from UAV-captured video sequences based on image registration. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bouhlel%2C+Fatma%22">Bouhlel, Fatma</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fatma.bouhlel@fss.usf.tn</i><br /><searchLink fieldCode="AR" term="%22Mliki%2C+Hazar%22">Mliki, Hazar</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hammami%2C+Mohamed%22">Hammami, Mohamed</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. May2024, Vol. 83 Issue 16, p46779-46798. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%2E+Defense+Advanced+Research+Projects+Agency%22">United States. Defense Advanced Research Projects Agency</searchLink><br /><searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The moving objects detection from freely moving camera like the one mounted on Unmanned Aerial Vehicle (UAV) stands as an important and challenging issue. This paper introduced a new MOD-IR method for moving objects detection from UAV-captured video sequences. The proposed method consists of four steps: (1) feature extraction and matching, (2) frame registration, (3) moving objects detection and (4) moving objects detection post-processing. Our method stands out from those of the literature in a number of ways. First, we enhanced the method effectiveness and robustness by handling the constraints related to this field through extracting robust features, on the one hand, and automatically defining the optimum threshold, on the other. Second, we proposed an efficient method able to deal with real-time applications by extracting keypoint features instead of pixel-to-pixel model estimation, and by simulating the search for the matching features among multiple trees. Finally, we involved the quick-shift segmentation in parallel with the three first steps, in order to enhance and accelerate the moving objects detection task. Relying on quantitative and qualitative evaluations of the proposed method on a variety of sequences extracted from several datasets (such as DARPA VIVID-EgTest05, Hopkins 155, UCF Aerial Action, etc.), we assessed the performance of our method compared to the state-of-the-art reference methods. Furthermore, the time cost evaluation has enabled us to emphasize that our MOD-IR method is the optimal choice for real-time applications, owing to its lower computational time requirement compared to the reference methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-023-16667-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 46779 Subjects: – SubjectFull: United States. Defense Advanced Research Projects Agency Type: general – SubjectFull: Image registration Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Drone aircraft Type: general Titles: – TitleFull: MOD-IR: moving objects detection from UAV-captured video sequences based on image registration. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bouhlel, Fatma – PersonEntity: Name: NameFull: Mliki, Hazar – PersonEntity: Name: NameFull: Hammami, Mohamed IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 83 – Type: issue Value: 16 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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