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.

It is estimated that over 1 billion Closed-Circuit Television (CCTV) cameras are operational worldwide. The advertised main benefits of CCTV cameras have always been the same; physical security, safety, and crime deterrence. The current scale and rate of deployment of CCTV cameras bring additional research and technical challenges for CCTV forensics as well, as for privacy enhancements.This paper presents the first end-to-end system for CCTV forensics and feasible privacy-enhancing applications such as exposure measurement, CCTV route recovery, CCTV-aware routing/navigation, and crowd-sourcing. For this, we developed and evaluated four complex and distinct modules (CCTVCV [1], OSRM-CCTV [2], BRIMA [3], CCTV-Exposure [4]), all of which are novel, unique, peer-reviewed, and can be used either separately or within an integrated end-to-end system such as CCTV-FullyAware. We release all our artefacts as open-source/open data. We hope our work will bootstrap policy-driving discussions and large-scale applications such as CCTV forensics and privacy-enhancing technologies.