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In this article, we examine the installation of surveillance cameras in the Bedouin town of Hura in the Negev desert in Israel, analysing the complex interactions between the state and the local inhabitants, for whom the cameras were an opportunity to exercise agency. Considering the long history of Jewish colonialism in the Negev, one could have assumed that the new surveillance technology was imposed on the Bedouins against their will. However, in‐depth interviews with Hura inhabitants demonstrated that the municipality and tribal authorities were active players in deciding on the installation and location of the surveillance cameras. In light of the conflictual relations of the Bedouin population to Israeli state authorities, we argue that while the cameras placed the Bedouins of Hura under a new layer of surveillance, their installation could also be understood as a reaction to the ongoing neglect of Bedouin lives and possessions by the state. [ABSTRACT FROM AUTHOR]

From the U.S. to Colombia to China, millions of public surveillance cameras are at the core of crime prevention strategies. Yet, we know little about the effects of surveillance cameras on criminal behavior, especially in developing economies. We study an installation program in Medellín and find that the quasi‐random allocation of cameras led to a decrease in crimes and arrests. With no increase in the monitoring capacity and no chance to use camera footage in prosecution, these results suggest offenders were deterred rather than incapacitated. We test for spillovers and find no evidence of crime displacement or diffusion of benefits to surrounding locations. [ABSTRACT FROM AUTHOR]

Indoor positioning techniques have become very important in recent years. Due to the wide deployment of surveillance cameras, it has become feasible to use the videos for indoor positioning. The success of using this approach can also reduce the load of security persons of watching the monitors all the time. In this study, the authors propose a vision‐based indoor positioning system. The proposed method uses a frame processing technique and applies the Gaussian mixture learning for video background model. The foreground object can be extracted by using the background subtraction. Based on the foreground object, the objects can be tracked and used in the direct linear transform, and generate a bird's‐eye map with camera information. A real‐time demonstration has been also provided. It shows the tracing of the moving objects and the bird's‐eye view. [ABSTRACT FROM AUTHOR]

An ear recognition system represents a powerful tool in forensic applications. Even in case the facial characteristic of a suspect is partly or fully covered an image of the outer ear may suffice to reveal a subject's identity. In forensic scenarios imagery may stem from surveillance cameras of environments where image compression is common practice to overcome limitations of storage or transmission capacities. Yet, the impact of severe image compression on ear recognition has remained undocumented. In this work the authors analyse the influence of different state‐of‐the‐art image compression standards on ear detection and ear recognition algorithms. Evaluations conducted on an uncompressed ear database are considered with respect to different stages in the processing chain of an ear recognition system where compression may be applied, representing the most relevant forensic scenarios. Experimental results are discussed in detail highlighting the potential and limitations of automated ear recognition in presence of image compression. [ABSTRACT FROM AUTHOR]

Nowadays, surveillance cameras are widely installed in public places for security and law enforcement, but the video quality may be low because of the limited transmission bandwidth and storage capacity. In this study, the authors proposed a gait recognition method for extremely low‐quality videos, which have a frame‐rate at one frame per second (1 fps) and resolution of 32 × 22 pixels. Different from popular temporal reconstruction‐based methods, the proposed method uses the average gait image (AGI) over the whole sequence as the appearance‐based feature description. Based on the AGI description, the authors employed a large number of weak classifiers to reduce the generalisation errors. The performance can be further improved by incorporating the model‐based information into the classifier ensemble. The authors found that the performance improvement is directly proportional to the average disagreement level of weak classifiers (i.e. diversity), which can be increased by using the model‐based information. The authors evaluated the proposed method on both indoor and outdoor databases (i.e. the low‐quality versions of OU‐ISIR‐D and USF databases), and the results suggest that our method is more general and effective than other state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

Although action recognition is a widely studied field in computer vision, the recognitions of aggressive activities and crowd violence actions are comparatively less studied. Nowadays, so many surveillance cameras have been installed in the streets and there is a demand for intelligent crowd activity detection systems. A method for violence detection in videos is proposed. The primary contribution is a novel transfer learning‐based violence detector that gives promising results compared with the existing detectors. First, the optical flows of the input videos are computed via Lucas–Kanade method. Then, several 2D templates are constructed with overlapping optical flow magnitudes and orientations. These templates are supplied to a pre‐trained convolutional neural network as input and deep features of different layers are extracted. Cubic kernel support vector machine and subspace k‐nearest neighbours classifiers are trained for prediction and the proposed method is tested with three different datasets that commonly used in violence detection studies. [ABSTRACT FROM AUTHOR]