Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/7718
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tu, Nguyen Anh | - |
dc.contributor.author | Huynh-The, Thien | - |
dc.contributor.author | Wong, Kok-Seng | - |
dc.contributor.author | Demirci, Muhammed Fatih | - |
dc.contributor.author | Lee, Young-Koo | - |
dc.date.accessioned | 2021-09-11T15:59:09Z | - |
dc.date.available | 2021-09-11T15:59:09Z | - |
dc.date.issued | 2021 | en_US |
dc.identifier.issn | 0920-8542 | - |
dc.identifier.issn | 1573-0484 | - |
dc.identifier.uri | https://doi.org/10.1007/s11227-021-03865-7 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/7718 | - |
dc.description.abstract | Nowadays, the explosion of CCTV cameras has resulted in an increasing demand for distributed solutions to efficiently process the vast volume of video data. Otherwise, the use of surveillance when people are being watched remotely and recorded continuously has raised a significant threat to visual privacy. Using existing systems cannot prevent any party from exploiting unwanted personal data of others. In this paper, we develop an intelligent surveillance system with integrated privacy protection, where it is built on the top of big data tools, i.e., Kafka and Spark Streaming. To protect individual privacy, we propose a privacy-preserving solution based on effective face recognition and tracking mechanisms. Particularly, we associate body pose with face to reduce privacy leaks across video frames. The body pose is also exploited to infer person-centric information like human activities. Extensive experiments conducted on benchmark datasets further demonstrate the efficiency of our system for various vision tasks. | en_US |
dc.description.sponsorship | Social Policy Grant; Nazarbayev University | en_US |
dc.description.sponsorship | This work was supported by the Social Policy Grant and funded by the Nazarbayev University. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Journal of Supercomputing | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Intelligent video analytics | en_US |
dc.subject | Large-scale surveillance | en_US |
dc.subject | Visual privacy | en_US |
dc.subject | Human activity analysis | en_US |
dc.subject | Big data | en_US |
dc.subject | Apache spark | en_US |
dc.title | Toward Efficient and Intelligent Video Analytics With Visual Privacy Protection for Large-Scale Surveillance | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.authorid | 0000-0002-9172-2935 | - |
dc.identifier.wos | WOS:000650818100002 | en_US |
dc.identifier.scopus | 2-s2.0-85106247687 | en_US |
dc.institutionauthor | Demirci, Muhammed Fatih | - |
dc.identifier.doi | 10.1007/s11227-021-03865-7 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q2 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.3. Department of Computer Engineering | - |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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