Intelligent Embedded Vision for Summarization of Multiview Videos in IIoT

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dc.contributor.authorHussain, Tanveer
dc.contributor.authorMuhammad, Khan
dc.contributor.authorSer, Javier Del
dc.contributor.authorBaik, Sung Wook
dc.contributor.authorDe Albuquerque, Victor Hugo C.
dc.contributor.institutionIA
dc.date.issued2020-04
dc.descriptionPublisher Copyright: © 2005-2012 IEEE.
dc.description.abstractNowadays, video sensors are used on a large scale for various applications, including security monitoring and smart transportation. However, the limited communication bandwidth and storage constraints make it challenging to process such heterogeneous nature of Big Data in real time. Multiview video summarization (MVS) enables us to suppress redundant data in distributed video sensors settings. The existing MVS approaches process video data in offline manner by transmitting them to the local or cloud server for analysis, which requires extra streaming to conduct summarization, huge bandwidth, and are not applicable for integration with industrial Internet of Things (IIoT). This article presents a light-weight convolutional neural network (CNN) and IIoT-based computationally intelligent (CI) MVS framework. Our method uses an IIoT network containing smart devices, Raspberry Pi (RPi) (clients and master) with embedded cameras to capture multiview video data. Each client RPi detects target in frames via light-weight CNN model, analyzes these targets for traffic and crowd density, and searches for suspicious objects to generate alert in the IIoT network. The frames of each client RPi are encoded and transmitted with approximately 17.02% smaller size of each frame to master RPi for final MVS. Empirical analysis shows that our proposed framework can be used in industrial environments for various applications such as security and smart transportation and can be proved beneficial for saving resources.11[Online]. Available: https://github.com/tanveer-hussain/Embedded-Vision-for-MVS.en
dc.description.sponsorshipManuscript received May 11, 2019; revised July 29, 2019; accepted August 7, 2019. Date of publication August 27, 2019; date of current version January 17, 2020. This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (2019R1A2B5B01070067). Paper no. TII-19-1832. (Corresponding author: Sung Wook Baik.) T. Hussain and S. W. Baik are with Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul 143-747, South Korea (e-mail:,tanveer445@ieee.org; sbaik@sejong.ac.kr).
dc.description.statusPeer reviewed
dc.format.extent11
dc.identifier.citationHussain , T , Muhammad , K , Ser , J D , Baik , S W & De Albuquerque , V H C 2020 , ' Intelligent Embedded Vision for Summarization of Multiview Videos in IIoT ' , IEEE Transactions on Industrial Informatics , vol. 16 , no. 4 , 8815938 , pp. 2592-2602 . https://doi.org/10.1109/TII.2019.2937905
dc.identifier.doi10.1109/TII.2019.2937905
dc.identifier.issn1551-3203
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85078699287&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Industrial Informatics
dc.relation.projectIDNational Research Foundation of Korea, NRF
dc.relation.projectIDMinistry of Science and ICT, South Korea, MSIT, 2019R1A2B5B01070067-TII-19-1832
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsArtificial intelligence
dc.subject.keywordsBig Data
dc.subject.keywordsInternet of Things (IoT)
dc.subject.keywordscomputational intelligence
dc.subject.keywordscomputer vision
dc.subject.keywordsconvolutional neural network (CNN)
dc.subject.keywordsindustrial Internet of Things (IIoT)
dc.subject.keywordsvideo summarization
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.titleIntelligent Embedded Vision for Summarization of Multiview Videos in IIoTen
dc.typejournal article
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