  In order to withstand the ever-increasing invasion of privacy by CCTV cameras
and technologies, on par CCTV-aware solutions must exist that provide privacy,
safety, and cybersecurity features. We argue that a first important step
towards such CCTV-aware solutions must be a mapping system (e.g., Google Maps,
OpenStreetMap) that provides both privacy and safety routing and navigation
options. However, this in turn requires that the mapping system contains
updated information on CCTV cameras' exact geo-location, coverage area, and
possibly other meta-data (e.g., resolution, facial recognition features,
operator). Such information is however missing from current mapping systems,
and there are several ways to fix this. One solution is to perform CCTV camera
detection on geo-location tagged images, e.g., street view imagery on various
platforms, user images publicly posted in image sharing platforms such as
Flickr. Unfortunately, to the best of our knowledge, there are no computer
vision models for CCTV camera object detection as well as no mapping system
that supports privacy and safety routing options.
  To close these gaps, with this paper we introduce CCTVCV -- the first and
only computer vision MS COCO-compatible models that are able to accurately
detect CCTV and video surveillance cameras in images and video frames. To this
end, our best detectors were built using 8387 images that were manually
reviewed and annotated to contain 10419 CCTV camera instances, and achieve an
accuracy of up to 98.7%. Moreover, we build and evaluate multiple models,
present a comprehensive comparison of their performance, and outline core
challenges associated with such research.
