A4 Artikkeli konferenssijulkaisussa
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-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Turtiainen, Hannu; Costin, Andrei; Hämäläinen, Timo; Lahtinen, Tuomo; Sintonen, Lauri
Emojulkaisu: TrustCom 2022 : Proceedings of the IEEE 21st International Conference on Trust, Security and Privacy in Computing and Communications
Konferenssin paikka ja aika: Wuhan, China, 9.-11.12.2022
ISBN: 978-1-6654-9426-7
eISBN: 978-1-6654-9425-0
Lehti tai sarja: IEEE International Conference on Trust, Security and Privacy in Computing and Communications
ISSN: 2324-898X
eISSN: 2324-9013
Julkaisuvuosi: 2022
Artikkelin sivunumerot: 1219-1226
Kustantaja: IEEE
Julkaisumaa: Yhdysvallat (USA)
Julkaisun kieli: englanti
DOI: https://doi.org/10.1109/trustcom56396.2022.00169
Julkaisun avoin saatavuus: Ei avoin
Julkaisukanavan avoin saatavuus:
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/86088
Tiivistelmä
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.
YSO-asiasanat: kameravalvonta; kamerat; konenäkö; koneoppiminen; tekoäly; yksityisyys; tietosuoja; kasvontunnistus (tietotekniikka); sovellukset (soveltaminen); tekninen rikostutkinta
Vapaat asiasanat: CCTV; cameras; computer vision; datasets; machine learning; mapping; object detection; privacy; video surveillance
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Raportointivuosi: 2023
Alustava JUFO-taso: 1
