A4 Article in conference proceedings
CCTVCV : Computer Vision model/dataset supporting CCTV forensics and privacy applications (2022)


Turtiainen, H., Costin, A., Hämäläinen, T., Lahtinen, T., & Sintonen, L. (2022). CCTVCV : Computer Vision model/dataset supporting CCTV forensics and privacy applications. In TrustCom 2022 : Proceedings of the IEEE 21st International Conference on Trust, Security and Privacy in Computing and Communications (pp. 1219-1226). IEEE. IEEE International Conference on Trust, Security and Privacy in Computing and Communications. https://doi.org/10.1109/trustcom56396.2022.00169


JYU authors or editors


Publication details

All authors or editorsTurtiainen, Hannu; Costin, Andrei; Hämäläinen, Timo; Lahtinen, Tuomo; Sintonen, Lauri

Parent publicationTrustCom 2022 : Proceedings of the IEEE 21st International Conference on Trust, Security and Privacy in Computing and Communications

Place and date of conferenceWuhan, China9.-11.12.2022

ISBN978-1-6654-9426-7

eISBN978-1-6654-9425-0

Journal or seriesIEEE International Conference on Trust, Security and Privacy in Computing and Communications

ISSN2324-898X

eISSN2324-9013

Publication year2022

Pages range1219-1226

PublisherIEEE

Publication countryUnited States

Publication languageEnglish

DOIhttps://doi.org/10.1109/trustcom56396.2022.00169

Publication open accessNot open

Publication channel open access

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/86088


Abstract

The increased, widespread, unwarranted, and unaccountable use of Closed-Circuit TeleVision (CCTV) cameras globally has raised concerns about privacy risks for the last several decades. Recent technological advances implemented in CCTV cameras, such as Artificial Intelligence (AI)-based facial recognition and Internet of Things (IoT) connectivity, fuel further concerns among privacy advocates. Machine learning and computer vision automated solutions may prove necessary and efficient to assist CCTV forensics of various types. In this paper, we introduce and release the first and only computer vision models are compatible with Microsoft common object in context (MS COCO) and capable of accurately detecting CCTV and video surveillance cameras in street view, generic images, and video frames. Our best detectors were built using 8,387 images, which were manually reviewed and annotated to contain 10,419 CCTV camera instances, and achieved an accuracy rate of up to 98.7%. This work proves fundamental to a handful of present and future applications that we discuss, such as CCTV forensics, pro-active detection of CCTV cameras, providing CCTV-aware routing, navigation, and geolocation services, and estimating their prevalence and density globally and on geographic boundaries.


Keywordsclosed-circuit televisioncamerascomputer visionmachine learningartificial intelligenceprivacydata protectionfacial recognition (computer science)applications (applying)forensic criminal investigation

Free keywordsCCTV; cameras; computer vision; datasets; machine learning; mapping; object detection; privacy; video surveillance


Contributing organizations


Ministry reportingYes

Reporting Year2023

Preliminary JUFO rating1


Last updated on 2024-03-04 at 17:46