DeepReS: A Deep Learning-Based Video Summarization Strategy for Resource-Constrained Industrial Surveillance Scenarios

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dc.contributor.authorMuhammad, Khan
dc.contributor.authorHussain, Tanveer
dc.contributor.authorDel Ser, Javier
dc.contributor.authorPalade, Vasile
dc.contributor.authorDe Albuquerque, Victor Hugo C.
dc.contributor.institutionIA
dc.date.issued2020-09
dc.descriptionPublisher Copyright: © 2005-2012 IEEE.
dc.description.abstractThe exponential growth in the production of video contents in different industries causes an urgent need for effective video summarization (VS) techniques, in order to get an optimal storage and preservation of key information in the video. Compared to other domains, industrial videos are more challenging to process, as they usually contain diverse and complex events, which make their online processing a difficult task. In this article, we introduce an online system for intelligent video capturing, coarse and fine redundancy removal, and summary generation. First, we capture video data through resource-constrained devices in an industrial Internet of Things network, equipped with vision sensors and apply coarse redundancy removal through the comparison of low-level features. Second, we transmit the resulting frames to the cloud for detailed analysis, where sequential features are extracted for the selection of candidate keyframes. Finally, we refine the candidate keyframes in order to discriminate those with maximum information as part of the summary. The key contributions of this article include the coarse and fine refining of video data implemented over resource-restricted devices and the presentation of important data in the form of a summary. Experiments11[Online]. Available: https://github.com/tanveer-hussain/DeepRes-Video-Summarization. over publicly available datasets evince a 0.3-unit increase in the F1 score when compared to state-of-the-art and with reduced time complexity. Furthermore, we provide convincing results on our newly created dataset in an industrial environment, which is made publicly available for the research community along with its labeled ground truth.en
dc.description.sponsorshipManuscript received July 28, 2019; revised October 23, 2019; accepted October 31, 2019. Date of publication December 18, 2019; date of current version May 26, 2020. This work was supported in part by the Brazilian National Council for Research and Development (CNPq) under Grants 304315/2017-6 and 430274/2018-1. The work of Javier Del Ser was supported by the Department of Education of the Basque Government through the Consolidated Research Group MATHMODE (IT1294-19). Paper no. TII-19-3387. (Corresponding author: Khan Muhammad.) K. Muhammad and T. Hussain are with the Department of Software, Sejong University, Seoul 143-747, South Korea (e-mail: khan.muhammad@ieee.org; tanveer445@ieee.org).
dc.description.statusPeer reviewed
dc.format.extent10
dc.identifier.citationMuhammad , K , Hussain , T , Del Ser , J , Palade , V & De Albuquerque , V H C 2020 , ' DeepReS : A Deep Learning-Based Video Summarization Strategy for Resource-Constrained Industrial Surveillance Scenarios ' , IEEE Transactions on Industrial Informatics , vol. 16 , no. 9 , 8936419 , pp. 5938-5947 . https://doi.org/10.1109/TII.2019.2960536
dc.identifier.doi10.1109/TII.2019.2960536
dc.identifier.issn1551-3203
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85081048977&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Industrial Informatics
dc.relation.projectIDDepartment of Education of the Basque Government, TII-19-3387-IT1294-19
dc.relation.projectIDConselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, 430274/2018-1-304315/2017-6
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsBig data
dc.subject.keywordscomputer vision
dc.subject.keywordsdeep learning
dc.subject.keywordsindustrial Internet of Things (IIoT)
dc.subject.keywordsresource-constrained devices
dc.subject.keywordsvideo summarization (VS)
dc.subject.keywordsControl and Systems Engineering
dc.subject.keywordsInformation Systems
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsElectrical and Electronic Engineering
dc.subject.keywordsSDG 9 - Industry, Innovation, and Infrastructure
dc.titleDeepReS: A Deep Learning-Based Video Summarization Strategy for Resource-Constrained Industrial Surveillance Scenariosen
dc.typejournal article
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