Real-time Video Surveillance Research Articles

Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.

Deep learning-based video surveillance system for suspicious activity detection

The need for reliable video surveillance systems to detect and prevent suspicious activities has become more important with the increase in crime and security threats. This paper proposes a real-time video surveillance system based on the Long-term Recurrent Convolutional Network (LRCN) model, which can automatically detect and alert the authority about suspicious activities, such as fighting, accidents, and robbery. Our system comprises two main components: LRCN-based activity recognition and real-time alert generation. We evaluated the performance of the proposed system on a custom dataset compiled from two publicly available datasets and achieved state-of-the-art results in terms of accuracy, precision, and recall. Our results demonstrate the effectiveness and scalability of the LRCN-based video surveillance system for real-time suspicious activity detection. We believe that our proposed system can be deployed in various public places, such as airports, train stations, and shopping malls, to enhance the security and safety of the public.

Real Time Weapon Detection using YOLOv8 and Alert Mechanism

Abstract: Security cameras and video surveillance cameras have become an important partof public safety. However, in many cities, thesesystems still manually detect high-risk situations. Understaffing in security services can lead to delays in detecting incidents or unforeseen threats, putting the public at risk. The aim of this project is to develop a low-cost, effective intelligencebased solution for real-time weapons detection and surveillance video analysis in different situations. As can be seen from many statistics, the incidence of gun and dangerous weapon crimes is increasing every year, making it difficult for the police to solve the problem in time. Crimes caused by guns or knives are very common in many places, especially in places where gun laws do not exist. Early detection of crime is critical to public safety. One way to prevent these situations is to use video surveillance to detect the presence of dangerous weapons such as guns and knives. Monitoring and control now also require monitoring and intervention.

Real Time Weapon Detection using YOLOv8 and Alert Mechanism

Abstract: Security cameras and video surveillance cameras have become an important partof public safety. However, in many cities, thesesystems still manually detect high-risk situations. Understaffing in security services can lead to delays in detecting incidents or unforeseen threats, putting the public at risk. The aim of this project is to develop a low-cost, effective intelligencebased solution for real-time weapons detection and surveillance video analysis in different situations. As can be seen from many statistics, the incidence of gun and dangerous weapon crimes is increasing every year, making it difficult for the police to solve the problem in time. Crimes caused by guns or knives are very common in many places, especially in places where gun laws do not exist. Early detection of crime is critical to public safety. One way to prevent these situations is to use video surveillance to detect the presence of dangerous weapons such as guns and knives. Monitoring and control now also require monitoring and intervention. Here, we present in video a system for tracking weapons suitable for tracking and controlling targets. We use the YOLOv8 (look once) algorithm to detect weapons in live video. YOLO model is an end-to-end deep learning model; it is very popular because it is fast and accurate. Previous methods such as region-based convolutional neural networks (R-CNN) required thousands of network tests to make predictions for an image, which could be time-consuming-Optimization is a laborious and painful process. He focuses on specific areas ofpainting and trains everything personally. The YOLO model, on the other hand, passes the image through the neural network only once. Since speed isimportant in real-time video, we use the YOLOv8 algorithm. This data was trained to classify three groups of weapons (pistols, knives, and artillery). When a weapon is detected, an alert is sent to authorities who can take action and reduce crime before it becomes a crime. Powered by Tensorflow, the system took 294 seconds in the first test toidentify weapon types in 9 categories.

Enhancing Security with Arduino uno-Powered Patrolling Robot

The development of a patrol robot using an Arduino Uno, ultrasonic sensors, PIR sensors, and the ESP32-CAM module represents an innovative approach to surveillance and automation. This autonomous robot is designed to navigate and secure designated areas efficiently. The ultrasonic sensors enable obstacle avoidance, while the PIR sensors detect motion accurately. The ESP32-CAM module provides real-time video surveillance capabilities. The robot is controlled and monitored remotely through a user-friendly interface, making it ideal for applications such as home security, monitoring, and automated patrolling. Future enhancements may involve integrating advanced navigation algorithms, and cloud-based data analysis to further expand its capabilities and adaptability.

Smart Surveillance System for Highways

Abstract: In today's world, everyone on the planet is concerned about security. Every citizen wishes to eliminate the possibility of a future threat to himself or his possessions. This tremendous increase in the need for security needs for individuals and organizations necessitates the installation of an appropriate and up-to-date surveillance system. Real-time video surveillance has become an essential component of the transportation system. It also prevents accidents and other difficult issues on highways for a transportation system. With numerous advancements, various machine learning and artificial intelligence-based techniques are progressing to handle such issues via smart surveillance systems. Various researchers have contributed various techniques for vehicle detection and tracking to the literature. These methods are both effective and time-consuming. For the safety and security of people and their resources, proper surveillance is critical. The innovation of an aerial surveillance system could be very efficient at addressing surveillance system challenges. Current systems are both costly and complex. A cost-effective and efficient solution that is easily accessible to anyone with a moderate budget is required. Highways are a critical component for transportation purpose, and it is necessary to make them as secure as possible.

Squid Examiner: Student Behavior Analysis Exam Surveillance System

The Squid Examiner system is an innovative solution designed to enhance examination security through automated cheating detection using real-time video surveillance. While offering real-time monitoring and detailed reporting, it faces privacy concerns, potential accuracy issues, and high initial costs. The system's future scope includes AI improvements, multi-modal surveillance, and global expansion, with a focus on continuous innovation and feedback from educational institutions and students.

Security Drone for Surveillance in Military

Abstract: This paper presents the design and development of a sophisticated military security drone equipped with advanced components including Pixhawk flight controller, GPS module, camera, receiver, telemetry system, BLDC (Brushless Direct Current) motors, and electronic speed controller (ESC). The Pixhawk flight controller serves as the brain of the drone, managing flight dynamics, navigation, and mission planning with remarkable precision and reliability. Integrated with a highaccuracy GPS module, the drone achieves autonomous navigation capabilities, enabling it to execute pre-defined flight paths and perform missions with minimal human intervention. A high-resolution camera system is incorporated into the drone, providing real-time video surveillance and reconnaissance capabilities. This camera system is complemented by a receiver module, facilitating remote control and data transmission between the drone and ground control station.Furthermore, the telemetry system enables remote monitoring of the drone's vital parameters, including altitude, speed, battery status, and sensor readings. This real-time telemetry data enhances situational awareness and facilitates informed decision-making during missions.The propulsion system of the drone consists of BLDC motors and electronic speed controllers, offering efficient and reliable propulsion for sustained flight operations. These components are carefully integrated to optimize power consumption, flight endurance, and maneuverability, ensuring the drone's effectiveness in various operational scenarios. In summary, the integration of Pixhawk flight controller, GPS module, camera, receiver, telemetry, BLDC motors, and electronic speed controller in the design of the military security drone represents a significant advancement in unmanned aerial technology. The resulting drone platform offers enhanced capabilities for surveillance, reconnaissance, and security applications, contributing to the effectiveness and efficiency of military operations in diverse environments

Real-time video surveillance on highways using combination of extended Kalman Filter and deep reinforcement learning

Highways, as one of the main arteries of transit and transportation in today's world, play a fundamental role in accelerating transportation, and for this reason, continuous monitoring of them is of great importance. Among these, monitoring compliance with transportation laws by vehicles is of utmost importance; for automation, efficient and vehicle-specific models can be used. In this article, a new method for video surveillance of highways is presented using an extended Kalman filter (EKF) and reinforcement learning models. There are three primary stages to the suggested approach. During the first stage, the extended Kalman filter (EKF) is used to identify and track multiple targets. Next, in the second stage, a convolutional neural network (CNN) processes each detected moving item to determine the kind of vehicle. During this stage, the CNN model's ideal configuration is ascertained using a new optimization approach that combines Particle Swarm Optimization (PSO) and reinforcement learning. After identifying the type of vehicle, in the third phase, the proposed method uses a separate CNN model for each target vehicle to assess its compliance with transportation safety principles. It should be mentioned that each vehicle's associated CNN model is configured during this phase using the suggested optimization methodology. Investigations have been conducted into the effectiveness of the suggested method in identifying violations of road safety laws as well as how well it performed in the two phases of vehicle type identification. According to the findings, the suggested approach can identify the kind of vehicle with 98.72% accuracy, which is at least 3.41% better than the approaches that were compared. On the other hand, this model can detect the violation of road safety laws for each vehicle with an average accuracy of 91.5%, which shows at least a 3.49% improvement compared to the other methods.

Real-time video surveillance based human fall detection system using hybrid haar cascade classifier

Real-time video surveillance based human fall detection system using hybrid haar cascade classifier

An improved performance of RetinaNet model for hand-gun detection in custom dataset and real time surveillance video

An improved performance of RetinaNet model for hand-gun detection in custom dataset and real time surveillance video

Twenty-five years of real-time surveillance video analytics: a bibliometric review

Twenty-five years of real-time surveillance video analytics: a bibliometric review

Digital identification and pattern recognition capabilities using machine learning methods, navigation systems, and video surveillance

The objects of the study are unmanned vehicles and branches of the bridge of the city of Kyiv (Ukraine), which connects the Great Ring Road, Zhytomyr Highway and Peremogy Avenue. The built routes were analyzed using the technology of recognition of road signs, people and vehicles. The important problem of this research is to analyze the possibilities of detecting obstacles by an unmanned vehicle using pattern recognition, which combines the methods of machine communication, navigation and real-time video surveillance. Based on the study, the results of detecting and avoiding obstacles on the road, where a study was conducted to investigate the main reasons that can cause time delays (traffic jams, weather conditions, accidents). The results of planning and navigation are obtained to determine the appropriate road route, which allows detecting and eliminating obstacles on the road, as well as building a map plan of the route in advance using online map services (Google Maps). It is shown that recognition of road signs (based on the classification using a road sign map consisting of 7 categories), people and vehicles minimizes the occurrence of road accidents, traffic jams and time delays. To recognize the images of road signs, people and vehicles, we studied the road sections connecting to the branched bridge. Thus, the authors have reviewed and analyzed the digital capabilities of pattern identification and recognition using machine learning methods, navigation and video surveillance systems, where the safety of vehicles with detection of road signs and obstacles on the way is of great importance. The results obtained can complement the possibilities of using unmanned vehicles to avoid obstacles and road accidents based on a trained pattern recognition system. This system, using convolutional neural networks and video surveillance navigation systems, will be able to provide the driver and the people around it with safe driving conditions.

Improved privacy protection technique for enhancing security of real-time video surveillance

Improved privacy protection technique for enhancing security of real-time video surveillance

Machine Learning for Real Time Video Surveillance

This paper presents a machine learning approach to realtime video surveillance, identifying and tracking objects using deep learning models.

Optimizing YOLOv8 for Real-Time CCTV Surveillance: A Trade-off Between Speed and Accuracy

Real-time video surveillance, especially CCTV systems, requires fast and accurate face detection. Object detection models with slow inference times are ineffective in real-time. This study addresses this challenge by improving the inference speed of the YOLOv8 model, a leading object detection framework known for its accuracy and speed. We focus on pruning the model's architecture, particularly the P5 head section, which detects larger objects. According to Bochkovskiy's 2020 research, this modification enhances the model's performance specifically for medium and small objects in CCTV footage. The standard YOLOv8 model and its modified version were compared for inference time, mean Average Precision (mAP), and model weight. The pruned YOLOv8 model cuts inference time by 15.56%, from 4.5 ms to 3.8 ms, and reduces model weight. The advantages mentioned above are offset by a 1.6% decrease in mean average precision. This research advances object detection technology by demonstrating architectural modifications' efficacy. These changes make the model faster and lighter, making it suitable for real-time surveillance. The accuracy trade-off is slight. The implications of these findings are crucial for implementing efficient object detection systems in CCTV surveillance. These findings also lay the groundwork for future research to improve such systems' speed-accuracy trade-off.

Security standards for real time video surveillance and moving object tracking challenges, limitations, and future: a case study

Security standards for real time video surveillance and moving object tracking challenges, limitations, and future: a case study

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas.

A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol.

Dynamic method to optimize memory space requirement in real-time video surveillance using convolution neural network

ABSTRACT Real-time video surveillance is one of the most effective ways to observe crime, mischief, and violence. But, most of the recent surveillance system consumes huge memory space to store the video. This article proposed an advanced dynamic video surveillance strategy to utilize minimum memory space with the finest detection of suspicious moving objects. In the proposed system, a convolution neural network (CNN) classifier and frame resolution switching is used to detect the movement of objects with appropriate frame resolution. A high-definition (HD) frame can be recorded dynamically when any movement of a suspicious object is detected. The system records low-quality frames, which are less important. Finally, the improved gradient-based histogram equalization technique is applied to all frames to obtain enhanced suspicious frames. Several real-time imperial tests are conducted and it is observed that the proposed system detects suspicious objects with 98.25% accuracy. Besides, the system consumes 80% less memory storage.

Optimized Small Waterbird Detection Method Using Surveillance Videos Based on YOLOv7

Waterbird monitoring is the foundation of conservation and management strategies in almost all types of wetland ecosystems. China’s improved wetland protection infrastructure, which includes remote devices for the collection of larger quantities of acoustic and visual data on wildlife species, increased the need for data filtration and analysis techniques. Object detection based on deep learning has emerged as a basic solution for big data analysis that has been tested in several application fields. However, these deep learning techniques have not yet been tested for small waterbird detection from real-time surveillance videos, which can address the challenge of waterbird monitoring in real time. We propose an improved detection method by adding an extra prediction head, SimAM attention module, and sequential frame to YOLOv7, termed as YOLOv7-waterbird, for real-time video surveillance devices to identify attention regions and perform waterbird monitoring tasks. With the Waterbird Dataset, the mean average precision (mAP) value of YOLOv7-waterbird was 67.3%, which was approximately 5% higher than that of the baseline model. Furthermore, the improved method achieved a recall of 87.9% (precision = 85%) and 79.1% for small waterbirds (defined as pixels less than 40 × 40), suggesting a better performance for small object detection than the original method. This algorithm could be used by the administration of protected areas or other groups to monitor waterbirds with higher accuracy using existing surveillance cameras and can aid in wildlife conservation to some extent.