Camera Surveillance as a Measure of Counterterrorism?

Camera surveillance has recently gained prominence in policy proposals on combating terrorism. We evaluate this instrument of counterterrorism as resting on the premise of a deterrence effect. Based on comparative arguments and previous evidence on crime, we expect camera surveillance to have a relatively smaller deterrent effect on terrorism than on other forms of crime. In particular, we emphasize opportunities for substitution (i.e., displacement effects), the interaction with media attention aspired to by terrorists, the limits of real-time interventions, the crowding-out of social surveillance, the risk of misguided profiling, and politico-economic concerns regarding the misuse of the technology.Camera surveillance, closed-circuit television (CCTV), public security, deterrence, terrorism

Camera Surveillance as a Measure of Counterterrorism?

Camera surveillance has recently gained prominence in policy proposals on combating terrorism. We evaluate this instrument of counterterrorism as resting on the premise of a deterrence effect. Based on comparative arguments and previous evidence on crime, we expect camera surveillance to have a relatively smaller deterrent effect on terrorism than on other forms of crime. In particular, we emphasize opportunities for substitution (i.e., displacement effects), the interaction with media attention aspired to by terrorists, the limits of real-time interventions, the crowding-out of social surveillance, the risk of misguided profiling, and politico-economic concerns regarding the misuse of the technology.Camera surveillance, closed-circuit television (CCTV), public security, deterrence, terrorism.

Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy

In this paper we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer's attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multi-stream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g. activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.Comment: Accepted to IEEE Transactions on Image Processin

Effective CCTV and the challenge of constructing legitimate suspicion using remote visual images

This paper compares the effectiveness of public CCTV systems according to meta-reviews, with what might be expected based upon theoretical predictions. The apparent gulf between practice and prediction is explored in the light of the challenges faced by CCTV operators in terms of effective target selection. In addition, counter-intuitive reactions by members of the public to situational symbols of crime deterrence may also undermine the efficacy of CCTV. Evidence is introduced and reviewed that suggests CCTV operators may employ implicit profiles to select targets. Essentially, young, scruffy males who appear to be loitering are disproportionately targeted compared with their base rate use of surveyed areas. However, the extent to which such a profile is diagnostic of criminal intent or behaviour is unclear. Such profiles may represent little more than ‘pattern matching’ within an impoverished visual medium. Finally, suggestions for future research and effective CCTV operator practice are offered in order to improve target selection.Peer reviewe

Visibility and the Policing of Public Space

From studies of ‘panoptic’ CCTV surveillance to accounts of undercover police officers, it is often mooted that visibility and invisibility are central to the policing of public space. However, there has been no comprehensive and critical assessment of this axiom. Drawing on the practices of a variety of policing providers and regulators, and the work of geographers, criminologists and other social scientists, this paper examines how and why visibility underpins the policing of public space. We begin by considering the ways in which policing bodies and technologies seek to render themselves selectively visible and invisible in the landscape. The paper then moves on to explore the ways in which policing agents attempt to make ‘incongruous’ bodies, behaviours and signs variously visible and invisible in public space. We then offer a sympathetic critique of these accounts, arguing that more attention is needed in understanding: (i) how other senses such as touch, smell and sound are socially constructed as in and out-of-place and ‘policed’ accordingly; and (ii) how the policing of undesirable bodies and practices is not simply about quantitative crime reduction, but conducted through qualitative, embodied performance. The paper concludes by pinpointing key areas for future research

The Fourth Amendment in the Twenty-First Century: Technology, Privacy, and Human Emotions

Police and local political officials in Tampa FL argued that the FaceIt system promotes safety, but privacy advocates objected to the city\u27s recording or utilizing facial images without the victims\u27 consent, some staging protests against the FaceIt system. Privacy objects seem to be far more widely shared than this small protest might suggest

The Fourth Amendment in the Twenty-First Century: Technology, Privacy, and Human Emotions

Police and local political officials in Tampa FL argued that the FaceIt system promotes safety, but privacy advocates objected to the city\u27s recording or utilizing facial images without the victims\u27 consent, some staging protests against the FaceIt system. Privacy objects seem to be far more widely shared than this small protest might suggest

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction