Existing Video Surveillance Systems Research Articles

Exploring Public's perception of safety and video surveillance technology: A survey approach

Addressing public safety effectively requires incorporating diverse stakeholder perspectives, particularly those of the community, which are often underrepresented compared to other stakeholders. This study presents a comprehensive analysis of the community's general public safety concerns, their view of existing surveillance technologies, and their perception of AI-driven solutions for enhancing safety in urban environments, focusing on Charlotte, NC. Through a survey approach, including in-person surveys conducted in August and September 2023 with 410 participants, this research investigates demographic factors such as age, gender, ethnicity, and educational level to gain insights into public perception and concerns toward public safety and possible solutions. Based on the type of dependent variables, we utilized different statistical and significance analyses, such as logit regression and ordinal logistic regression, to explore the effects of demographic factors on the various dependent variables. Our results reveal demographic differences in public safety concerns. Younger females tend to feel less secure yet trust existing video surveillance systems, whereas older, educated individuals are more concerned about violent crimes in malls. Additionally, attitudes towards AI-driven surveillance differ: older Black individuals demonstrate support for it despite having concerns about data privacy, while educated females show a tendency towards skepticism.

IMPROVING THE EFFICIENCY OF RESPONSE TO GAS, OIL AND WATER SHOWS AND OPEN FOUNTAINS BY CREATING A VIDEO SURVEILLANCE SYSTEM FOR GAS WELL WORKOVER

The oil and gas industry is one of the most important branches of the national economy. It differs from other branches of the fuel and energy complex in high capital investments and long construction and operation periods. One of the main tasks in gas industry enterprise management is the most efficient use of capital investments, which requires the adoption of informed decisions in oil and gas asset management. Gas industry enterprises include a large number of hazardous production facilities, accidents and incidents at which are the most dangerous for both personnel and population in general, as well as for the industry companies themselves. Emergencies at facilities can lead to financial and reputational losses, as well as damage the health and life of people and the environment. Therefore, the issue of planning measures for prevention, as well as localization and elimination of the accident consequences, is considered one of the priorities.The presence of video surveillance at production facilities is an integral part of an integrated security system. High-quality video surveillance helps to identify deviations in automated processes in a timely manner and take measures to eliminate failures and violations. Timely and rapid response to emergency situations prevents the occurrence of an accident, as well as prevents socio-economic damage.The authors have conducted many studies in the sphere of analysis of the organization of localization and liquidation of emergency situations and the effectiveness of personnel response in emergency situations at hazardous production facilities. Besides, studies of video surveillance systems in Gazprom UGS LLC branches – Departments of Emergency Recovery and Well Overhaul – have been conducted. Gazprom UGS LLC is the only operator of 23 underground gas storage facilities on the territory of the Russian Federation. Most of the Company's production facilities are located at a considerable distance from settlements and other infrastructure facilities, which increases the response time to emergency situations, and as a result, may lead to an emergency situation. Therefore, it is necessary to constantly control and monitor the integrity of the system. The authors have made conclusions about the need to improve existing video surveillance systems and expand their use at other branches of the Company.

Privacy-protecting behaviours of risk detection in people with dementia using videos

Background People living with dementia often exhibit behavioural and psychological symptoms of dementia that can put their and others’ safety at risk. Existing video surveillance systems in long-term care facilities can be used to monitor such behaviours of risk to alert the staff to prevent potential injuries or death in some cases. However, these behaviours of risk events are heterogeneous and infrequent in comparison to normal events. Moreover, analysing raw videos can also raise privacy concerns. Purpose In this paper, we present two novel privacy-protecting video-based anomaly detection approaches to detect behaviours of risks in people with dementia. Methods We either extracted body pose information as skeletons or used semantic segmentation masks to replace multiple humans in the scene with their semantic boundaries. Our work differs from most existing approaches for video anomaly detection that focus on appearance-based features, which can put the privacy of a person at risk and is also susceptible to pixel-based noise, including illumination and viewing direction. We used anonymized videos of normal activities to train customized spatio-temporal convolutional autoencoders and identify behaviours of risk as anomalies. Results We showed our results on a real-world study conducted in a dementia care unit with patients with dementia, containing approximately 21 h of normal activities data for training and 9 h of data containing normal and behaviours of risk events for testing. We compared our approaches with the original RGB videos and obtained a similar area under the receiver operating characteristic curve performance of 0.807 for the skeleton-based approach and 0.823 for the segmentation mask-based approach. Conclusions This is one of the first studies to incorporate privacy for the detection of behaviours of risks in people with dementia. Our research opens up new avenues to reduce injuries in long-term care homes, improve the quality of life of residents, and design privacy-aware approaches for people living in the community.

Smart Security Camera using Machine Learning

Numerous applications have been found for video surveillance. Smart video surveillance systems can improve situational awareness at various scales of time and space. It describes a remote control and surveillance architecture based on mobile devices. To record camera photos and detect intrusion using an image comparison technique, this project uses the Opency library. The streamed video is sent from the server to the remote administrator over the phone after the comparison is completed and an intrusion is discovered. Afterward, admin can take the necessary action. Automatic video analytics are used in smart surveillance to increase the efficiency of security measures. By automatically identifying the individual's deviant conduct, this system complements the existing video surveillance systems while also introducing intelligent analysis of single person activity to improve home security. By sending an SMS, the user is informed and the pertinent data is recorded. The user is able to watch the specific video. This system keeps the home secure, which lowers the frequency of burglary cases and improves social stability.

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

SummaryIn recent decades, wireless sensor networks (WSNs) are employed in the remote monitoring system which experiences a huge delay due to the excessive time consumption by the image processing techniques. The existing video surveillance system is unable to detect the anomaly accurately and falls short in classifying the routine activity and the anomaly activities. Therefore, an efficient anomaly detection model is necessitated for identifying the anomaly behavior of malicious nodes in the WSN environment. This research work introduces a novel artificial intelligence (AI)‐based remote monitoring system for WSN. Primarily, the online video frames are retrieved from the wireless video sensors where the frames are generated by segmenting the entire stream of video as a constant‐sized structural frame. The sequence/stream of video segments is fed into the proposed AI framework to detect anomaly activities. It has multiple levels of hidden layers to compare the features of the input captured image with the preloaded reference image. Furthermore, a 5‐fold cross‐validation approach has been inspected to validate the efficacy of the suggested AI model. The performance results evident that the proposed AI system acquires a better prediction accuracy of 98.2% and precision of 97.3% than the existing monitoring systems.

ViDMASK dataset for face mask detection with social distance measurement

The COVID-19 outbreak has extenuated the need for a monitoring system that can monitor face mask adherence and social distancing with the use of AI. With the existing video surveillance systems as base, a deep learning model is proposed for mask detection and social distance measurement. State-of-the-art object detection and recognition models such as Mask RCNN, YOLOv4, YOLOv5, and YOLOR were trained for mask detection and evaluated on the existing datasets and on a newly proposed video mask detection dataset the ViDMASK. The obtained results achieved a comparatively high mean average precision of 92.4% for YOLOR. After mask detection, the distance between people’s faces is measured for high risk and low risk distance. Furthermore, the new large-scale mask dataset from videos named ViDMASK diversifies the subjects in terms of pose, environment, quality of image, and versatile subject characteristics, producing a challenging dataset. The tested models succeed in detecting the face masks with high performance on the existing dataset, MOXA. However, with the VIDMASK dataset, the performance of most models are less accurate because of the complexity of the dataset and the number of people in each scene. The link to ViDMask dataset and the base codes are available at https://github.com/ViDMask/VidMask-code.git.

ViTrack: Efficient Tracking on the Edge for Commodity Video Surveillance Systems

Nowadays, video surveillance systems are widely deployed in various places, e.g., schools, parks, airports, roads, etc. However, existing video surveillance systems are far from full utilization due to high computation overhead in video processing. In this work, we present ViTrack, a framework for efficient multi-video tracking using computation resource on the edge for commodity video surveillance systems. In the heart of ViTrack lies a two layer spatial/temporal compressed target detection method to significantly reduce the computation overhead by combining videos from multiple cameras. Further, ViTrack derives the video relationship and camera information even in absence of camera location, direction, etc. To alleviate the impact of variant video quality and missing targets, ViTrack leverages a Markov Model based approach to efficiently recover missing information and finally derive the complete trajectory. We implement ViTrack on a real deployed video surveillance system with 110 cameras. The experiment results demonstrate that ViTrack can provide efficient trajectory tracking with processing time 45x less than the existing approach. For 110 video cameras, ViTrack can run on a Dell OptiPlex 390 computer to track given targets in almost real time. We believe ViTrack can enable practical video analysis for widely deployed commodity video surveillance systems.

Application of 5G communication technology on intelligent inspection in 750kV substation

Ultra-high voltage AC substation has the characteristics of high voltage level, large area, complex equipment structure, heavy inspection task and high operation risk. The existing video surveillance system is mainly used for substation security, but the distribution of points is insufficient and the clarity is low, so it can’t achieve full coverage of inspection points. UHV AC substation needs to develop remote intelligent inspection system to assist operation and maintenance staffs to carry out daily inspection work, reduce the workload of operation and maintenance staffs, improve the speed of emergency response and ensure the safety of operation. In this paper, remote intelligent inspection system of substation based on 5G communication transmission technology is proposed in 750kV substation, so as to achieve the purpose of intelligent inspection of substation, and expand the idea for the application of 5G technology in substation.

Design of an intelligent video surveillance system for crime prevention: applying deep learning technology

As the security threat and crime rate have been increased all over the globe, the video surveillance system using closed-circuit television (CCTV) has become an essential tool for many security-related applications and is widely used in many areas as a monitoring system. However, most of the data collected by the video surveillance system is used as evidence of objective data after crime and disaster have occurred. And, often time, video surveillance systems tend to be used in a passive manner due to the high cost and human resources. The video surveillance system should actively respond to detect crime and accidents in advance through real-time monitoring and immediately transmit data in case of an accident. This study proposes developing an intelligent video surveillance system that can actively monitor in real-time without human input. In solving the problems of the existing video surveillance system, deep learning technology will be carried through the data processing model design to visualize data for crime detection after building an artificial intelligence server and video surveillance camera. In addition, this design proposes an intelligent surveillance system to quickly and effectively detect crimes by sending a video image and notification message to the web through real-time processing.

Image-based occupancy positioning system using pose-estimation model for demand-oriented ventilation

Dynamic changes in the position, number, and activities of occupants create various indoor scenarios that affect the operation of and energy required for heating, ventilation, and air conditioning (HVAC) systems. HVAC systems can use less energy by adapting their operational parameters according to the occupant distribution. However, accurate and non-intrusive occupancy positioning systems have yet to be established for conventional HVAC and more recently developed demand-oriented ventilation (DOV) systems. Herein, a novel image-based occupancy positioning system is proposed. This system uses multiple cameras to capture images from different views and a computer to perform digital image processing and three-dimensional (3D) reconstruction. Data fusion and 3D reconstruction algorithms were developed based on skeleton key points extracted from the images with a human pose estimation model. The entire system was built and tested in a multi-function room under two different scenarios. The results showed that the system provides positioning and orientation information for a dozen of people in real time. Because the system requires an ordinary computer and conventional surveillance cameras, it can be integrated into existing video surveillance systems to avoid privacy issues. As the speed of digital image processing and computer calculations increases, the cost of the system will decrease; these characteristics provide a solid foundation for the operation of highly efficient DOV systems.

State of the art of consumer video surveillance standards in Russia and abroad

In recent years, video surveillance technology has been developing. The use of video surveillance has spread from national and social welfare to everyday life, and a number of consumer-grade video surveillance products have been created. However, there is a problem where the domestic market is still in a non-standard stage. Consumer-grade video surveillance system does not have specific standards, which seriously affect the consumer awareness of the consumer-grade video surveillance products. This article presents a video surveillance system and reveals the standards of an existing video surveillance system.

Method of non-stop passage of signal-controlled intersections using dynamic signs and computer vision

Nowadays, methods ensuring non-stop traffic do not take into account the number of out-group vehicles located in front of an intersection, waiting for the green light, the geometry of road network junctions, and specifics of traffic management at them. The existing methods propose to apply empirical formulas that do not account for the above-mentioned features of intersections and approaches to them. Besides, route vehicles in a flow and their capacity, trucks and road trains, and the possibility of sharing lanes by both kinds of vehicles have a large influence on the operation of traffic lights. Due to these disadvantages, a traffic flow moving in a coordinated mode stops despite the green light. The article develops a method for calculating the duration from the moment when the green light is turned on at the previous road network junction to the moment when the green light is turned on at the next intersection. The method allows using existing video surveillance systems to monitor traffic and obtain information on traffic conditions changing in real-time and the exact number and real structure of the out-group traffic flow. It is proposed to ensure non-stop coordinated traffic using dynamic road signs indicating the upper speed limit at a road section towards a signal-controlled road network junction.

Visual Measurement of Water Level under Complex Illumination Conditions.

Image-based water level measurement is a visual-sensing technique which automatically inspects the reading of the water line via image processing instead of the human eye. It can be realized easily on an existing video surveillance system and has advantages like low cost, non-contact, as well as results that are verifiable. It has the potential to be widely used in flood and waterlogging monitoring, while facing the challenge that water-line detection under complex natural or artificial illumination conditions is quite difficult in field applications. To handle this problem, a method is proposed assuming that the water line is generally located on the row with the largest local change of gray or edge features in the image of the water gauge. The water line is determined by coarse-to-fine detection of the position of the maximum mean difference (MMD) of the horizontal projections of gray and edge images. Image-based flow-level measurement systems were developed at two measurement sites. In situ comparative experiments were conducted with the float-type stage gauge and other image-based methods. The results show that the fusion of gray and edge features can overcome the shortcomings of single feature methods under complex illumination conditions such as dim light, glares, shadows and artificial night lighting. A coarse-to-fine strategy utilizes the periodicity of the surface pattern distribution of the standard bicolor water gauge, which improves the reliability of water-line detection. The resolution and accuracy of water-level measurement are 1 mm and 1 cm, respectively. In particular, the MMD value is efficient at identifying extremely unfavorable conditions and reducing gross errors.

Building a post-compression region-of-interest encryption framework for existing video surveillance systems

We propose an encryption framework design and implementation which add region-of-interest encryption functionality to existing video surveillance systems with minimal integration and deployment effort. Apart from region-of-interest detection, all operations take place at bit-stream level and require no re-compression whatsoever. This allows for very fast encryption and decryption speed at negligible space overhead. Furthermore, we provide both objective and subjective security evaluations of our proposed encryption framework. Furthermore, we address design- and implementation-related challenges and practical concerns. These include modularity, parallelization and, most notably, the performance of state-of-the-art face detectors. We find that their performance, despite their frequent use in surveillance systems, is not insufficient for practical purposes, both in terms of speed and detection accuracy.

Study on the Application of Video Surveillance System of Expressway Construction Based on 3G

Safety and quality supervision is an important part of expressway construction, the traditional regulation mode relies on artificial which exists objectivity and poor oversight, poor technical means and other issues, which brought a significant security risks to the project quality and safety. In this paper, based on the analysis of the existing video surveillance system, We explored the application of 3G technology in video surveillance system in the expressway construction. First of all, Composition and principle of the system are discussed in this paper. Secondly, the platform constitute of wireless video surveillance system is analyzed in detail, Finally, We summarized the application advantages of 3G technology in the expressway construction ,which is of great significance to enhance Expressway construction management.

A suspicious behaviour detection using a context space model for smart surveillance systems

Video surveillance systems using Closed Circuit Television (CCTV) cameras, is one of the fastest growing areas in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. This work attempts to address these problems by proposing an automatic suspicious behaviour detection which utilises contextual information. The utilisation of contextual information is done via three main components: a context space model, a data stream clustering algorithm, and an inference algorithm. The utilisation of contextual information is still limited in the domain of suspicious behaviour detection. Furthermore, it is nearly impossible to correctly understand human behaviour without considering the context where it is observed. This work presents experiments using video feeds taken from CAVIAR dataset and a camera mounted on one of the buildings Z-Block) at the Queensland University of Technology, Australia. From these experiments, it is shown that by exploiting contextual information, the proposed system is able to make more accurate detections, especially of those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information gives critical feedback to the system designers to refine the system.

Bypass methods for constructing robust automatic human tracking system

We have been proposing the automatic human tracking system to overcome limitations of existing video surveillance systems. This paper proposes two bypass methods to keep tracking a targeted person by the system constructed by using mobile agent techn