Closed Circuit Television Surveillance Systems Research Articles

Cost-Effectiveness of CCTV Surveillance Systems: Evidence from a Polish City

Closed-circuit television (CCTV) surveillance has been increasingly popular worldwide as a crime prevention measure, while its effectiveness, efficiency, and related privacy issues are debated. In this paper, we investigated whether the municipal CCTV surveillance system is cost-effective in the case study of Poznan, Poland. The analysis comprised (a) the preventive effectiveness of CCTV, (b) costs of CCTV surveillance system installation and operation, and (c) costs of crime. We collected police data on three categories of street crimes: (a) car burglary/theft, (b) car damage, and (c) robbery, as well as data on the costs of CCTV surveillance system construction and maintenance. We searched for a year after installing the CCTV system when the accumulated, hypothetical financial cost of crime was higher than the actual accumulated cost of crime and CCTV installation and maintenance in any treatment area. The number of crimes in a given category was predicted based on a comparable control area, where there were no cameras. Our study suggests that CCTV surveillance is cost-ineffective in most areas. This result implies the cautious development of CCTV surveillance, taking into account the target crime types and costs, including social costs and benefits. Evaluating cost-effectiveness, although considered as a robust measure, is in practice difficult. However, analysis of the correlation between land use characteristics and cost-effectiveness may provide practical advice on where to develop CCTV surveillance monitoring.

Towards Proactive Surveillance through CCTV Cameras under Edge-Computing and Deep Learning

Weapons, usually a handgun, a revolver, or a pistol, are used in the majority of criminal acts. The traditional closed-circuit television (CCTV) surveillance and control system requires human intervention to detect such crime incidents. The purpose of this research is to develop a real-time automatic weapon carrier detection system that may be used with CCTV cameras and surveillance systems. The goal is to alarm and alert the security officials to take proactive action to prevent violent activities. In deep learning literature, region-based classifiers (R-FCN and Faster R-CNN) and regression-based detectors (Yolo invariant) are being used as promising object detection methods. Although region-based classifiers are accurate, they lack the speed of detection required for real-time detection, whereas regression-based detectors (for example, YoloV4 invariant) are fast enough for real-time detection, but lack accuracy. The method applied in this study relies on Yolov4 to quickly detect anomalies, followed by R-FCN to boost detection accuracy by filtering out any false positives. A weapon dataset comprising 4430 locally and internationally available weapon photos with a 70–30 split ratio is used to train and test the system, which is subsequently evaluated using a live surveillance camera system. This hybrid system achieved a 90% accuracy with a low false positive rate, as well as 94% precision, 86% recall, and 89% F1 score. Our results prove that the proposed hybrid system is useful for proactive real-time surveillance to alarm the existence of a suspicious weapon carrier in a surveillance area.

Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset

High-dynamic-range (HDR) imaging is a digital image processing technique that enhances an image’s visibility by modifying its color and contrast ranges. Generative adversarial networks (GANs) have proven to be potent deep learning models for HDR imaging. However, obtaining a sufficient volume of training image pairs is difficult. This problem has been solved using CycleGAN, but the result of the use of CycleGAN for converting a low-dynamic-range (LDR) image to an HDR image exhibits problematic color distortion, and the intensity of the output image only slightly changes. Therefore, we propose a GAN training optimization model for converting LDR images into HDR images. First, a gamma shift method is proposed for training the GAN model with an extended luminance range. Next, a weighted loss map trains the GAN model for tone compression in the local area of images. Then, a regional fusion training model is used to balance the training method with the regional weight map and the restoring speed of local tone training. Finally, because the generated module tends to show a good performance in bright images, mean gamma tuning is used to evaluate image luminance channels, which are then fed into modules. Tests are conducted on foggy, dark surrounding, bright surrounding, and high-contrast images. The proposed model outperforms conventional models in a comparison test. The proposed model complements the performance of an object detection model even in a real night environment. The model can be used in commercial closed-circuit television surveillance systems and the security industry.

Real-time video fire/smoke detection based on CNN in antifire surveillance systems

This work presents a real-time video-based fire and smoke detection using YOLOv2 Convolutional Neural Network (CNN) in antifire surveillance systems. YOLOv2 is designed with light-weight neural network architecture to account the requirements of embedded platforms. The training stage is processed off-line with indoor and outdoor fire and smoke image sets in different indoor and outdoor scenarios. Ground truth labeler app is used to generate the ground truth data from the training set. The trained model was tested and compared to the other state-of-the-art methods. We used a large scale of fire/smoke and negative videos in different environments, both indoor (e.g., a railway carriage, container, bus wagon, or home/office) or outdoor (e.g., storage or parking area). YOLOv2 is a better option compared to the other approaches for real-time fire/smoke detection. This work has been deployed in a low-cost embedded device (Jetson Nano), which is composed of a single, fixed camera per scene, working in the visible spectral range. There are not specific requirements for the video camera. Hence, when the proposed solution is applied for safety on-board vehicles, or in transport infrastructures, or smart cities, the camera installed in closed-circuit television surveillance systems can be reused. The achieved experimental results show that the proposed solution is suitable for creating a smart and real-time video-surveillance system for fire/smoke detection.

An Automatic Shoplifting Detection from Surveillance Videos (Student Abstract)

The use of closed circuit television (CCTV) surveillance devices is increasing every year to prevent abnormal behaviors, including shoplifting. However, damage from shoplifting is also increasing every year. Thus, there is a need for intelligent CCTV surveillance systems that ensure the integrity of shops, despite workforce shortages. In this study, we propose an automatic detection system of shoplifting behaviors from surveillance videos. Instead of extracting features from the whole frame, we use the Region of Interest (ROI) optical-flow fusion network to highlight the necessary features more accurately.

Conditional effects of open-street closed-circuit television (CCTV) on crime: A case from Korea

Prior studies reported mixed findings concerning the effects of Closed-Circuit Television (CCTV) on crime reduction. By utilizing the multi-contextual criminal opportunity theory, this study aims to determine the conditional effects of open-street CCTV surveillance system on crime across socioeconomic and physical environmental factors. Using the data from one district in Seoul, Korea, we ran a series of Poisson regression models with interaction terms allowed for articulating the conditional effects of CCTVs on crime. In line with studies in Western countries, CCTVs in Korea were significantly associated with levels of property crime and the areas with larger surveillance zones had lower property crime levels. Also, there was a conditional crime reduction effect of CCTVs on assaults, depending on the areal density of alcohol outlets. The conditional effect of CCTVs suggests some neighborhood characteristics optimize the deployment of CCTVs, which guide more effective implementation of CCTV systems for crime control and prevention.

Real-time video traffic management for a warship combat system

In an effort to improve the channel utilization of a warship combat system (WCS), it is necessary to control and manage the large volume of video traffic by considering a strong self-similarity characteristic. We propose a new video traffic management method for the WCS that smooths the traffic of the closed-circuit television surveillance system (CSS) video and encodes the electro-optical tracking system (EOTS) video with low complexity in real time. First, to achieve global and local traffic smoothing for the CSS video traffic, a proper bandwidth allocation interval and a minimum bandwidth for the CSS video traffic must be determined. Second, due to the increased available bandwidth after traffic smoothing, more elaborate low-delay encoding is accomplished by selecting encoding modes adaptively to compress the EOTS video efficiently with high visual quality. The experimental results demonstrate that the proposed method is able to control the WCS traffic to be as flat as possible and to encode the EOTS video with low delay, even in combat and emergency scenarios. Hence, we show that to migrate to network-centric warfare successfully, the proposed method is much more suitable for the WCS than conventional video traffic management.

PROGRESS IN COMPUTATIONAL INTELLIGENCE TO SUPPORT CCTV SURVEILLANCE SYSTEMS

The development and capabilities of closed circuit television surveillance systems in association with distributed computing systems are reviewed, and the applications to various aspects of surveillance are described.

Efficient and effective automated surveillance agents using kernel tricks

Many schemes have been presented over the years to develop automated visual surveillance systems. However, these schemes typically need custom equipment, or involve significant complexity and storage requirements. In this paper we present three software-based agents built using kernel machines to perform automated, real-time intruder detection in surveillance systems. Kernel machines provide a powerful data mining technique that may be used for pattern matching in the presence of complex data. They work by first mapping the raw input data onto a (often much) higher-dimensional feature space, and then clustering in the feature space instead. The reasoning is that mapping onto the (higher-dimensional) feature space enables the comparison of additional, higher-order correlations in determining patterns between the raw data points. The agents proposed here have been built using algorithms that are adaptive, portable, do not require any expensive or sophisticated components, and are lightweight and efficient having run times of the order of hundredths of a second. Through application to real image streams from a simple, run-of-the-mill closed-circuit television surveillance system, and direct quantitative performance comparison with some existing schemes, we show that it is possible to easily obtain high detection accuracy with low computational and storage complexities.

CCTV and Citizen Guardianship Suppression: A Questionable Proposition

An untested hypothesis regarding closed-circuit television (CCTV) is that the use of CCTV surveillance systems causes the loss of informal citizen guardianship activities in camera-surveilled public spaces. This hypothesized effect is empirically tested in two ways. In the first approach, an examination of survey responses between respondents who were aware, without prompting, of a public space surveillance camera system (24.4% of the respondents) and those who were not aware of the cameras is undertaken. The expectation is that preexisting knowledge of the cameras will be associated with attitudes associated with a self-reported reduced willingness to exercise guardianship actions. In the second approach, time series data sets of calls for service in a CCTV-surveilled area and a comparison control zone are examined. Neither method revealed empirical evidence of a degrading of informal citizen guardianship activities following the installation of CCTV cameras.

Tracking-based event detection for CCTV systems

This paper presents a description of an event detection algorithm based on trajectories designed for closed-circuit television (CCTV) surveillance systems. Following the foreground segmentation, blob and scene basic characteristics—blob position or speed and people density—are used to create low-level descriptions of predefined events. Comparing sequence parameters with the semantic description of the events associated with the current scenario, the system is able to detect them and raise an alert signal to the operator, the final decision-maker. In the approach presented here, the specific demands for CCTV surveillance systems applied to public transport environments will be analysed, together with the appropriate image processing techniques in order to build an intelligent surveillance system able to detect “potentially dangerous situations.”

The implementation and impact of crime prevention / crime control open street Closed-Circuit Television surveillance in South African Central Business Districts

The use and implementation of public open street Closed Circuit Television (CCTV) surveillance systems in Central Business Districts (CBDs) in South Africa solely for the purpose of crime control (reducing street crime) or crime prevention (deterrence) has in South Africa been a relatively new intervention within the broader context of crime prevention programmes. One of the drawbacks to its implementation for this purpose has been its costs and the inability of the South African Police Service to fund such implementation in the light of other more pressing priorities and demands on its finances and resources. However, the initiative to start implementing and linking CCTV surveillance systems in CBDs in the major metropolitan cities of South Africa to local police services was taken in the mid-1990s by Business Against Crime of South Africa (BACSA). This article, using case study overviews from four South African CBD areas (Cape Town, Johannesburg, Pretoria (Tshwane) and Durban), traces CCTV use as crime control or prevention surveillance, how they were implemented, the rationale behind their implementation and the operationalising of them in terms of preventing street crime and its uses in other surveillance. In addition it also looks at this initiative from the perspective of the growth and commercialisation of the management of these services, and the co-operation and co-ordination structures in partnership with the South African Police Service (SAPS). Furthermore, it reviews the purported impact on the reduction of crime of these systems in CBDs and finally the application of public crime surveillance by the CCTV control room operators (private security) in co-operation with the police (response team) and the role it plays in the observation, recording, arrest and conviction of suspects.

CCTV surveillance systems in town and city centre management

During the 1990s, closed circuit television (CCTV) surveillance technology has been an increasingly important and visible part of crime and nuisance prevention measures in town and city centres throughout the UK. The paper outlines the principal characteristics of CCTV systems and identifies the objectives guiding their introduction. The Home Office, police forces and many local authorities have expressed support for CCTV and they often report reductions in reported crime levels and in the fear of crime following the introduction of CCTV. At the same time, there are concerns about the cost‐effectiveness of CCTV and about a number of legal, personal privacy and civil liberties issues.