Video Surveillance System Research Articles

Violence Detection in Jails and Mental Asylums

This research investigates the use of Long- term Recurrent Convolutional Networks (LRCNs) for violence detection in video surveillance systems. LRCNs combine the strengths of Convolutional Neural Networks (CNNs) for capturing spatial information and Long Short-Term Memory (LSTM) networks for modeling temporal sequences. This combination allows the system to learn complex spatiotemporal patterns in video data, improving violence detection accuracy in environments like jails and mental health facilities. The project focuses on the integration of the LRCN model with a Telegram bot for real-time alerting and response. Upon detecting violent incidents in the video streams, the LRCN model triggers alerts through the Telegram bot, providing instant notifications to relevant authorities. The Telegram bot facilitates seamless communication and coordination among stakeholders, enabling swift action to mitigate potential risks and ensure the safety of occupants within these facilities. Through rigorous experimentation and evaluation, the effectiveness and reliability of the LRCN-based violence detection system integrated with the Telegram bot are demonstrated. The research contributes to advancing technology-driven solutions for proactive security measures in high-risk environments, fostering safer and more secure institutional settings

Leveraging Artificial Intelligence for Crime Detection and Prevention

This paper sheds light on how Artificial Intelligence (AI) is reshaping the landscape of crime detection and prevention, bringing about a significant change in traditional law enforcement methods. With society increasingly embracing AI-driven solutions, it's crucial to understand how they contribute to public safety. We'll dive into various aspects of AI, such as predictive policing, video surveillance analysis, forensic science, and criminal profiling, to see how they enhance law enforcement capabilities and streamline crime resolution processes.Starting with predictive policing, AI algorithms sift through massive datasets to identify patterns and predict future criminal activity, empowering law enforcement to take proactive measures. Similarly, AI-powered video surveillance systems enhance real-time monitoring and anomaly detection, allowing for quick identification of suspicious behavior in urban environments.Moving on, we'll explore AI's impact on forensic analysis, where it expedites processes like DNA sequencing, fingerprint recognition, and ballistics analysis, speeding up the resolution of cold cases. Additionally, we'll delve into AI's role in criminal profiling, highlighting its ability to decipher behavioral patterns and predict criminal motivations, though ethical considerations remain paramount.We'll also address ethical concerns surrounding AI deployment in law enforcement, including privacy issues and algorithmic biases. Emphasizing the need for transparency and accountability, we advocate for responsible use of AI-powered crime detection systems to uphold ethical standards and protect individual rights.

Hermes, a low-latency transactional storage for binary data streams from remote devices

In many contexts where data is streamed on a large scale, such as video surveillance systems, there is a dual requirement: secure data storage and continuous access to audio and video content by third parties, such as human operators or specific business logic, even while the media files are still being collected. However, using transactions to ensure data persistence often limits system throughput and latency. This paper presents a solution that enables both high ingestion rates with transactional data persistence and near real-time, low-latency access to the stream during collection. This immediate access enables the prompt application of specialized data engineering algorithms during data acquisition. The proposed solution is particularly suitable for binary data sources such as audio and video recordings in surveillance systems, and it can be extended to various big data scenarios via well-defined general interfaces. The scalability of the approach is based on the microservice architecture. Preliminary results obtained with Apache Kafka and MongoDB replica sets show that the proposed solution provides up to 3 times higher throughput and 2.2 times lower latency compared to standard multi-document transactions.

Normality learning reinforcement for anomaly detection in surveillance videos

Video Anomaly Detection (VAD) is a key technology that enables automatic anomaly detection in surveillance video systems. Due to the considerable dimensions and fine-grained spatial details of video sequences, VAD tasks necessitate the collaboration of learning both spatial and temporal contextualized information. Due to the restricted understanding of spatial details, previous unsupervised VAD works have yet to fully tap into the potential of prediction-based methods to fully capture the spatial–temporal correlations. Inspired by the lateral geniculate nucleus in visual processing, we propose a Normality Learning Reinforcement framework of VAD (NLR-VAD) that enables learning more sophisticated and efficient spatial–temporal interactions. Specifically, NLR-VAD introduces a Normality Learning Reinforcement Unit (NLRU) rooted in diffusion models. NLRU refines the irrelevant information and amplifies the knowledge retrieval of spatial normality, particularly in high-resolution details. NLRU collaborates with Memory-Augmented Auto-Encoder (MAAE) to enhance spatial–temporal normality through fine-grained extractions. Moreover, a cross-NLRU fusion unit is proposed for information integration as a bridge between NLRU and MAAE. Quantitative results on three benchmarks show that NLR-VAD performs competitively compared with the previous methods. Extended analysis and visualization fully demonstrate the effectiveness of the proposed units.

Adaptive Processing of Camera Video Stream with Limitations on the Network Data Transmission Bandwidth

Video surveillance systems, cameras, and video stream processing are actively used in many enterprises as a monitoring and control tool for regular and emergency situations, as well as staff activities. The application of intelligent algorithms allows tracking and minimizing operator errors, but these approaches are highly sensitive to the quality of the original video, presence of noise, and low resolution. On the other hand, such video surveillance systems may be limited by network bandwidth. Therefore, this work considers an adaptive video stream processing algorithm that ensures efficient operation of computer vision and object recognition methods while minimizing the amount of transmitted information within network bandwidth constraints. The proposed algorithm addresses the task of determining boundary conditions that ensure the functionality of object recognition algorithms with the least amount of video stream. Corresponding experimental studies were conducted to determine the minimum values of frame resolution and video bitrate. The algorithm was tested in organizing video surveillance at warehouse complexes. The obtained results can be used in developing decision support systems for enterprises in various industries requiring intelligent processing of large volumes of data.

Vulnerability analysis of super high-rise building security system based on Bayesian network and digital twin technology

Recent years have seen the increasing need to prevent illegal intrusions in super high-rise buildings. To improve the security system of super high-rise buildings by adding dynamic vulnerability assessments for illegal intrusion risks and rectification plan optimization capabilities, while also to increase its visualization level, a method utilizing digital twin technology and integrating Bayesian networks is proposed. To validate this method, a case study within a prominent super high-rise building in the Central Business District (CBD) of Beijing is conducted, in which the vulnerability analysis results have shown that among all the tertiary parent nodes, the coverage extent of the video surveillance system most significantly influences the vulnerability. By using the comprehensive blind spot analysis function of the established digital twin platform, thoughtful additions and angle adjustments of surveillance cameras have successfully increased the score of the security level from 84 to 90. This has proved that the method proposed in this study can provide decision support for the vulnerability assessment and improvement for the security system of super high-rise buildings.

Efficient Task-driven Video Data Privacy Protection for Smart Camera Surveillance System

As one of the most commonly used AIoT sensors, smart cameras and their supporting services, namely cloud video surveillance (CVS) systems, have brought great convenience to people’s lives. Recent CVS providers use different machine learning techniques to improve their services (regarded as tasks) based on the uploaded video. However, uploading data to the CVS providers may cause severe privacy issues. Existing works that remove privacy information could not achieve a high tradeoff between data usability and privacy, because the importance of information varies with the task. In addition, it is challenging to design a real-time privacy protection mechanism, especially in resource-constrained smart cameras. In this work, we design a task-driven and efficient video privacy protection mechanism for a better tradeoff between privacy and data usability. We use Class Activation Mapping to protect privacy while preserving data usability. To improve the efficiency, we utilize the motion vector and residual matrix produced during video codec. Our work outperforms the region of interest–based methods in data protection while preserving data usability. The attack accuracy drops 70%, while the task accuracy is comparable to those without protection (within ± 4%). The average protection frame rate of the High Definition video can exceed 16 fps+ even on a CPU.

CCTV Network based Crowd Management, Crime Prevention System using AI/ML

In today's world the rise in security concerns and criminal activity has prompted the widespread adoption of closed-circuit television (CCTV) as a vital component in security measures. This monitoring system is now utilized in various sectors to aid in the prevention and detection of potential threats. However due to the exponential cost of employing personnel video surveillance systems are largely employed in a passive manner. The majority of information gathered by these systems is only put to use as evidence after an incident of crime or disaster has transpired. In light of this we are implementing a more proactive approach. Our video surveillance system will actively respond using real-time monitoring and an alert system to prevent and mitigate the impact of crime and accidents before they occur

Person re-identification algorithm by image from video surveilance sistem using a neural network compound descriptor

For people re-identification in distributed video surveillance systems, it is important to have an algorithm that ensures efficiency when a person is blocked by other people or objects. Therefore, for this task, an algorithm has been developed that involves the compound descriptor formation, which includes a global features vector of a person’s image and three local ones for its upper, middle and down parts. The regions of interest selection is carried out based on the detecting key points results in the image of the human body. If a person's image part is occluded by other people or objects, then it is classified as invisible. The hidden part person image is not used in the formation of the local feature. To obtain it, the average value of the k-nearest neighbors such features of a person’s image is calculated. The experiments performed indicate an increase in re-identification accuracy for the Market-1501, DukeMTMC-ReID, MSMT17 and PolReID1077 datasets.

PEDESTRIAN ALERT SYSTEM USING DEEP LEARNING

Pedestrian detection is an essential and significant task in any intelligent video surveillance system, as it provides the fundamental information for semantic understanding of the video footages. It has an obvious extension to automotive applications due to the potential for improving safety systems. Many car manufacturers (e.g., Volvo, Ford, GM, Nissan) offer this as an ADAS option in 2017. It is used in many cars and vehicles for automatic driver assistance systems. But when the ADAS system detects pedestrians but if fails to apply brakes on time accidents might happened. To overcome this situation our project provides an audio alert to the human passengers or driver in the vehicle so that they can apply manual brakes. In this project we will require Python libraries such as Pytorch and OpenCV. Pytorch is a widely used Machine Learning library. It is popular for the YOLO (You Only Look Once) algorithm which is built for Object Detection, we are using the YOLO algorithm and customize it to detect objects on a pedestrian dataset. Python has a library pyttsx3, that is capable to convert text-to-speech offline. Itextracts the label from the detected pedestrians in the video and converts the text label into speech. The algorithm detects the pedestrians when they are in a close approach to the vehicles and sends the identifies labels to the pyttsx3 speaker engine. An audio alert is generated by the engine and alerts the passengers or drivers. Keywords - YOLO, Pytorch, Pyttsx3, Object Detection, Python.

Edge HPC Architectures for AI-Based Video Surveillance Applications

The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures. This article examines the impact of different platforms for HPC edge servers, including x86 and ARM CPU-based systems and Graphics Processing Units (GPUs), on the speed and accuracy of video processing tasks. By using advanced deep learning frameworks, a video surveillance system based on YOLO object detection and DeepSort tracking algorithms is developed and evaluated. This study thoroughly assesses the strengths, limitations, and suitability of different hardware architectures for various AI-based surveillance scenarios.

VIDEO SURVEILLANCE ON THE DRILLING RIG

The article describes a mobile video surveillance system on a drilling rig, which is designed to carry out technological control in high-risk areas at oil and gas producing enterprises. The advantages of this complex, circuit design solutions and functional properties of the device are presented. The field kit is installed directly at the facility in the hazardous area that needs to be monitored. The Polar Eye video surveillance system is designed to meet a number of industrial safety requirements at oil and gas production facilities. Video information from the danger zone is transmitted using the BSPD system, operating in the unlicensed 2.4 GHz frequency range, to the safe zone, where the analytical center for visual and operational control is located.The most important competitive advantages of the complex – low weight, compactness and mobility – were achieved by developing their own layout and circuit design solutions and by implementing most of the nodes on printed circuit boards of their own production.

Інформаційна технологія підтримки прийняття рішень в задачах відеоспостереження та моніторінгу

In the modern world, video surveillance systems are an integral part of security and monitoring measures in various sectors, from public places to industrial facilities. However, with their increasing prevalence, ethical issues arise concerning the use of video data and the impact of these systems on privacy and citizens' rights. In this article, we will delve into some of these issues in detail and propose recommendations for their resolution. It is important to recognize the consequences that video surveillance and monitoring can have on the quality and effectiveness of decisions made by people. This problem proves to be quite relevant in various fields, including manufacturing, engineering, and others. Video surveil-lance and monitoring have become an integral part of the modern world, particularly in the areas of security, transportation, urban planning, and ecology, with the aid of advanced video analysis and data processing technologies. Today's video surveillance systems provide a high level of information support for decision-making, which is why we will explore the key aspects of information technology that support decision-making in video surveillance and monitoring tasks, as well as the prospects and challenges of its application.

Smart Video Surveillance Using Deep Learning

Abstract: There is a lot of assessment happening in the business about video observation among them; the occupation of CCTV accounts has been blocked. CCTV cameras are put all around the spots for perception and security. In today's digital age, the increasing availability of data and advancements in computer vision have paved the way for numerous applications of object detection. This powerful technology has found its significance in various domains, including video surveillance and image retrieval systems. Object detection enables machines to identify and locate objects within images or video frames, providing valuable insights and aiding in decision-making processes. This article explores the applications, challenges, techniques, and future trends of object detection in the context of video surveillance and image retrieval systems. Object detection is a computer vision technique that involves identifying and localizing objects within images or video frames. Unlike image classification, which assigns a single label to an entire image, object detection goes a step further by detecting and delineating the individual objects present. It enables computers to comprehend visual data and interact with the world, much like humans do

Advancements in Intelligent Video Surveillance for Public Safety: A Comprehensive Review

Abstract: Intelligent video surveillance systems have emerged as invaluable tools for ensuring public safety in a variety of settings, from essential infrastructure to urban environments. These systems, which are driven by state-of-the-art technology like computer vision, machine learning, and artificial intelligence, provide unmatched real-time threat detection, analysis, and response capabilities. This study provides an in-depth analysis of the most recent developments in intelligent video surveillance for public safety, covering key technology, useful uses, enduring difficulties, and potential directions. This paper aims to provide a thorough knowledge of the significant implications and emerging opportunities of intelligent video surveillance in protecting public places from potential dangers through an examination of recent research insights and industry advancements. This article seeks to offer a guide for researchers, practitioners, policymakers, and stakeholders navigating the ever-changing field of intelligent video surveillance for public safety by exploring the evolving trends, emerging technologies, and practical applications.

도시 안전을 위한 블록체인 기반의 감시카메라 영상 관리 시스템 모델 및 설계 방법

This paper proposes a new approach to video surveillance systems, which have become essential components in modern urban management. By utilizing blockchain and IPFS, it enhances data integrity and privacy protection. Additionally, anomaly detection and automatic video storage are enabled through object detection technology, thus improving urban safety and security. This integrated approach serves as an efficient management methodology for surveillance systems, providing city administrators and citizens with a safer and more effective monitoring environment.

Video Processing Based Tracking and Vehicle Identification

Video surveillance and analysis have become integral components of various domains such as security, traffic management, and urban planning. However, effective tracking and identification of vehicles in video streams remain challenging due to environmental factors, occlusions, and complex motion patterns. This research proposes a novel approach leveraging YOLOv5, a state-of-the-art object detection algorithm, for real-time vehicle tracking and identification. By integrating YOLOv5 with advanced video processing techniques, including preprocessing for enhancing video quality and Kalman filtering for object tracking, the proposed system achieves improved accuracy and robustness in diverse scenarios. Experimental results demonstrate the effectiveness of the approach, showcasing high accuracy in vehicle tracking and reliable identification performance. The findings suggest significant potential for practical applications in enhancing video surveillance systems for better security and traffic management. Additionally, avenues for future research are discussed to further enhance the capabilities of video-based vehicle tracking and identification systems

Weapon Detection Using FRCNN

Abstract: Due to the increase in crime in remote and congested places, security is a top priority in all fields. Computer vision is having numerous uses for abnormal objects recognition and monitoring to deal with an array of scenarios. It's necessary for video surveillance systems to be employed as a way to fulfil the growing need and the protection of individual's financial assets, safety, and security, and to enhance having the capacity to detect and assess unusual events, is necessary for intelligence monitoring.This enables the utilization of FRCNN machine learning that autonomously identify weapons. Two different types of datasets will be utilized in the provided implementation. A pair of datasets can be downloaded, one with labelled photos and the second with a combination of manually labelled photos. In almost all of the aforementioned scenarios, algorithms gather precise data; nevertheless, how well speed and precision coincide could impact how algorithms are used in real-life instances

AMPLIFYING AQUATIC VISUALS

Our proposed fusion-based approach aims to combat the degradation of underwater images, addressing issues such as diminished colors and indistinguishable objects. Leveraging contrast stretching and Auto White Balance, the technique significantly improves contrast and color, offering a straightforward yet effective solution to enhance visibility in aquatic images, crucial for applications like video surveillance in outdoor computer vision systems. This straightforward approach plays a pivotal role in enhancing image visibility, contributing significantly to applications like video surveillance and other outdoor computer vision systems. Our dehazing process builds upon two essential statistical observations related to haze-free images and haze. By applying dark channel prior and guided filter to the decomposed image, we effectively estimate atmosphere light, resulting in a dehazed output. This method tackles the intricacies of haze, contributing to the generation of high-quality images with improved details and clarity. Beyond image enhancement, our approach showcases versatility by extending into object classification, demonstrating the broader impact and potential of our method in the realm of outdoor computer vision systems.

AUTOMATED ACCESS CONTROL SYSTEMS

The paper provides an overview and analysis of the problems in the application of automated access control systems based on NFC. The capabilities of the Arduino platform are investigated and an access control system with an RFID-NFC module is developed on its basis. The use of automated access control systems in the modern world is becoming increasingly relevant. It involves a number of benefits that such systems can bring to businesses and other entities. These systems simplify and speed up the process of identifying a person, save time and increase the efficiency of security services, but still require human control. RFID-NFC technology is up-to-date, so the development and use of this technology for an access control system can solve the problem of unauthorized access to premises and/or controlled areas. Also, a company/enterprise can save money on identification devices, since various documents with an embedded NFC tag can be used as an identifier. For example, it can be a passport of a citizen of Ukraine, a passport of a citizen of Ukraine for travelling abroad, a certificate of registration, bank cards and a smartphone with NFC function, etc. An automated access control system is a set of advanced technologies and software designed to control and restrict access to a specific private territory/controlled area. The main purpose of an automated access control system is to ensure security, control and monitoring of access to a private territory/controlled area. Such systems are used to identify personnel, vehicles, etc. that have access to a restricted area/controlled zone. Access control systems can be integrated with other security systems, such as video surveillance systems, intrusion detection/prevention systems, etc.