Efficient video summarization through MobileNetSSD: a robust deep learning-based framework for efficient video summarization focused on objects of interest.
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| Title: | Efficient video summarization through MobileNetSSD: a robust deep learning-based framework for efficient video summarization focused on objects of interest. |
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| Authors: | Yarrarapu, Manasa1 (AUTHOR) manasayarrarapu@gmail.com, Leelavathy, Narkedamilly2 (AUTHOR) drnleelavathy@gmail.com, Haritha, Dasari3 (AUTHOR) harithadasari9@yahoo.com |
| Source: | Multimedia Tools & Applications. Aug2025, Vol. 84 Issue 26, p30663-30688. 26p. |
| Subjects: | Video summarization, Object recognition (Computer vision), Time-domain analysis, Multimedia systems, Deep learning, Computer vision |
| Abstract: | Now-a-days, the generation of videos has increased dramatically due to the quick growth of multimedia and the internet. The need for effective ways to store, manage, and index the massive numbers of videos has become imperative due to this expansion. As a result, a method needs to be proposed that collects only the necessary data from the original recording. In computer vision, Video summarization is a significant task, and its primary goal is to give a quick summary of the video by removing irrelevant information and capturing key frames from the video. Many approaches have developed over the last several decades, using the most recent deep neural network architectures that represent the current state-of-the-art. Our method involves extracting vital key frames from the input video using the MobileNetSSD model, which is well-known for its efficient recognition and localization of objects of interest. These highlighted frames are essential in creating a detailed video summary. Furthermore, a method of temporal analysis is applied to guarantee that the summary accurately reflects the relevant events in the order in which they occurred, contributing to a coherent and meaningful representation of the information. We evaluated the proposed approach on TV Sum and SUM me video datasets, comparing the results against cutting-edge video summarization techniques. Our approach works effectively to produce clear and meaningful video summaries. [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: 187119184 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Efficient video summarization through MobileNetSSD: a robust deep learning-based framework for efficient video summarization focused on objects of interest. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yarrarapu%2C+Manasa%22">Yarrarapu, Manasa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> manasayarrarapu@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Leelavathy%2C+Narkedamilly%22">Leelavathy, Narkedamilly</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> drnleelavathy@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Haritha%2C+Dasari%22">Haritha, Dasari</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> harithadasari9@yahoo.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Aug2025, Vol. 84 Issue 26, p30663-30688. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Video+summarization%22">Video summarization</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Time-domain+analysis%22">Time-domain analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Multimedia+systems%22">Multimedia systems</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Now-a-days, the generation of videos has increased dramatically due to the quick growth of multimedia and the internet. The need for effective ways to store, manage, and index the massive numbers of videos has become imperative due to this expansion. As a result, a method needs to be proposed that collects only the necessary data from the original recording. In computer vision, Video summarization is a significant task, and its primary goal is to give a quick summary of the video by removing irrelevant information and capturing key frames from the video. Many approaches have developed over the last several decades, using the most recent deep neural network architectures that represent the current state-of-the-art. Our method involves extracting vital key frames from the input video using the MobileNetSSD model, which is well-known for its efficient recognition and localization of objects of interest. These highlighted frames are essential in creating a detailed video summary. Furthermore, a method of temporal analysis is applied to guarantee that the summary accurately reflects the relevant events in the order in which they occurred, contributing to a coherent and meaningful representation of the information. We evaluated the proposed approach on TV Sum and SUM me video datasets, comparing the results against cutting-edge video summarization techniques. Our approach works effectively to produce clear and meaningful video summaries. [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/s11042-024-20372-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 30663 Subjects: – SubjectFull: Video summarization Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Time-domain analysis Type: general – SubjectFull: Multimedia systems Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Computer vision Type: general Titles: – TitleFull: Efficient video summarization through MobileNetSSD: a robust deep learning-based framework for efficient video summarization focused on objects of interest. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yarrarapu, Manasa – PersonEntity: Name: NameFull: Leelavathy, Narkedamilly – PersonEntity: Name: NameFull: Haritha, Dasari IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 26 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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