Predicting and optimizing network recognition time: a multi-parameter fusion model for edge-side human behavior recognition.
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| Title: | Predicting and optimizing network recognition time: a multi-parameter fusion model for edge-side human behavior recognition. |
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| Authors: | Pang, Di1 (AUTHOR), Wei, Zhe1,2 (AUTHOR) weizhe@sut.edu.cn, Chen, Mo1 (AUTHOR) |
| Source: | Journal of Mechanical Science & Technology. Feb2025, Vol. 39 Issue 2, p795-803. 9p. |
| Subjects: | Artificial intelligence, Video surveillance, Deep learning, Network performance, Image processing, Virtual networks |
| Abstract: | Embedding the behavior recognition network into the edge-side video surveillance device can solve the problems of high latency, high computational resource consumption, and high cost of the cloud placement scheme. However, in placing the network on the edge side, Ignoring the relationship between network complexity and embedded devices will lead to the embedded devices failing to meet the identification network's requirements and the identification network's performance not meeting the scene's requirements. To address the above problem, we tested the recognition time of different recognition networks, explored the relationship between the recognition time of the network and the amount of network computation and parameters, used fitting tools to solve the relationship between the three variables, and combined the solved model with different hardware performance to modify it, propose a multiparameter fusion model of a behavior recognition network and realize the prediction of the recognition time of the recognition network. The model predicted a recognition time error of within 5 % in tests on UCF-101 dataset and production scene behavior dataset. Taking the design of the dangerous behavior identification network of CRRC Zhuzhou Locomotive Factory as an example, We use this model to predict and optimize the recognition network's recognition time, modify the recognition network's structure, and reduce the recognition time of the network. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Mechanical Science & Technology 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 183130739 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting and optimizing network recognition time: a multi-parameter fusion model for edge-side human behavior recognition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pang%2C+Di%22">Pang, Di</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wei%2C+Zhe%22">Wei, Zhe</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> weizhe@sut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Mo%22">Chen, Mo</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Mechanical+Science+%26+Technology%22">Journal of Mechanical Science & Technology</searchLink>. Feb2025, Vol. 39 Issue 2, p795-803. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Video+surveillance%22">Video surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Network+performance%22">Network performance</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+networks%22">Virtual networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Embedding the behavior recognition network into the edge-side video surveillance device can solve the problems of high latency, high computational resource consumption, and high cost of the cloud placement scheme. However, in placing the network on the edge side, Ignoring the relationship between network complexity and embedded devices will lead to the embedded devices failing to meet the identification network's requirements and the identification network's performance not meeting the scene's requirements. To address the above problem, we tested the recognition time of different recognition networks, explored the relationship between the recognition time of the network and the amount of network computation and parameters, used fitting tools to solve the relationship between the three variables, and combined the solved model with different hardware performance to modify it, propose a multiparameter fusion model of a behavior recognition network and realize the prediction of the recognition time of the recognition network. The model predicted a recognition time error of within 5 % in tests on UCF-101 dataset and production scene behavior dataset. Taking the design of the dangerous behavior identification network of CRRC Zhuzhou Locomotive Factory as an example, We use this model to predict and optimize the recognition network's recognition time, modify the recognition network's structure, and reduce the recognition time of the network. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Mechanical Science & Technology 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=183130739 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12206-025-0124-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 795 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Video surveillance Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Network performance Type: general – SubjectFull: Image processing Type: general – SubjectFull: Virtual networks Type: general Titles: – TitleFull: Predicting and optimizing network recognition time: a multi-parameter fusion model for edge-side human behavior recognition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pang, Di – PersonEntity: Name: NameFull: Wei, Zhe – PersonEntity: Name: NameFull: Chen, Mo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1738494X Numbering: – Type: volume Value: 39 – Type: issue Value: 2 Titles: – TitleFull: Journal of Mechanical Science & Technology Type: main |
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