Hannu Turtiainen

Hannu Turtiainen, Andrei Costin, Timo Hamalainen

In this work, we present CCTV-Exposure -- the first CCTV-aware solution to evaluate potential privacy exposure to closed-circuit television (CCTV) cameras. The objective was to develop a toolset for quantifying human exposure to CCTV cameras from a privacy perspective. Our novel approach is trajectory analysis of the individuals, coupled with a database of geo-location mapped CCTV cameras annotated with minimal yet sufficient meta-information. For this purpose, CCTV-Exposure model based on a Global Positioning System (GPS) tracking was applied to estimate individual privacy exposure in different scenarios. The current investigation provides an application example and validation of the modeling approach. The methodology and toolset developed and implemented in this work provide time-sequence and location-sequence of the exposure events, thus making possible association of the exposure with the individual activities and cameras, and delivers main statistics on individual's exposure to CCTV cameras with high spatio-temporal resolution.

Lauri Sintonen, Hannu Turtiainen, Andrei Costin et al.

For the last several decades, the increased, widespread, unwarranted, and unaccountable use of Closed-Circuit TeleVision (CCTV) cameras globally has raised concerns about privacy risks. Additional recent features of many CCTV cameras, such as Internet of Things (IoT) connectivity and Artificial Intelligence (AI)-based facial recognition, only increase concerns among privacy advocates. Therefore, on par \emph{CCTV-aware solutions} must exist that provide privacy, safety, and cybersecurity features. We argue that an important step forward is to develop solutions addressing privacy concerns via routing and navigation systems (e.g., OpenStreetMap, Google Maps) that provide both privacy and safety options for areas where cameras are known to be present. However, at present no routing and navigation system, whether online or offline, provide corresponding CCTV-aware functionality. In this paper we introduce OSRM-CCTV -- the first and only CCTV-aware routing and navigation system designed and built for privacy, anonymity and safety applications. We validate and demonstrate the effectiveness and usability of the system on a handful of synthetic and real-world examples. To help validate our work as well as to further encourage the development and wide adoption of the system, we release OSRM-CCTV as open-source.

Tuomo Lahtinen, Hannu Turtiainen, Andrei Costin

Image annotation and large annotated datasets are crucial parts within the Computer Vision and Artificial Intelligence fields.At the same time, it is well-known and acknowledged by the research community that the image annotation process is challenging, time-consuming and hard to scale. Therefore, the researchers and practitioners are always seeking ways to perform the annotations easier, faster, and at higher quality. Even though several widely used tools exist and the tools' landscape evolved considerably, most of the tools still require intricate technical setups and high levels of technical savviness from its operators and crowdsource contributors. In order to address such challenges, we develop and present BRIMA -- a flexible and open-source browser extension that allows BRowser-only IMage Annotation at considerably lower overheads. Once added to the browser, it instantly allows the user to annotate images easily and efficiently directly from the browser without any installation or setup on the client-side. It also features cross-browser and cross-platform functionality thus presenting itself as a neat tool for researchers within the Computer Vision, Artificial Intelligence, and privacy-related fields.

Tuomo Lahtinen, Lauri Sintonen, Hannu Turtiainen et al.

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 that provides both privacy and safety routing and navigation options. To the best of our knowledge, there are no mapping nor navigation systems that support privacy and safety routing options. In this paper, we explore the feasibility of a CCTV-aware routing and navigation solution. The aim of this feasibility exploration is to understand what are the main impacts of CCTV on privacy, and what are the challenges and benefits to building such technology. We evaluate our approach on seven (7) pedestrian walking routes within the downtown area of the city of Jyvaskyla, Finland. We first map a total of 450 CCTV cameras, and then experiment with routing and navigation under several different configurations to coarsely model the possible cameras' parameters and coverage from the real-world. We report two main results. First, our preliminary findings support the overall feasibility of our approach. Second, the results also reveal a data-driven worrying reality for persons wishing to preserve their privacy/anonymity as their main living choice. When modelling cameras at their low performance end, a privacy-preserving route has on average a 1.5x distance increase when compared to generic routing. When modelling cameras at their medium-to-high performance end, a privacy-preserving route has on average a 5.0x distance increase, while in some cases there are no privacy-preserving routes possible at all. These results further support and encourage both global mapping of CCTV cameras and refinements to camera modelling and underlying technology.

Hannu Turtiainen, Andrei Costin, Tuomo Lahtinen et al.

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.