Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7718
Title: Toward efficient and intelligent video analytics with visual privacy protection for large-scale surveillance
Authors: Tu, Nguyen Anh
Huynh-The, Thien
Wong, Kok-Seng
Demirci, Muhammed Fatih
Lee, Young-Koo
Keywords: Intelligent video analytics
Large-scale surveillance
Visual privacy
Human activity analysis
Big data
Apache spark
Issue Date: 2021
Publisher: Springer
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.
URI: https://doi.org/10.1007/s11227-021-03865-7
https://hdl.handle.net/20.500.11851/7718
ISSN: 0920-8542
1573-0484
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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