A system for traffic violation detection

This paper describes the framework and components of an experimental platform for an advanced driver assistance system (ADAS) aimed at providing drivers with a feedback about traffic violations they have committed during their driving. The system is able to detect some specific traffic violations, record data associated to these faults in a local data-base, and also allow visualization of the spatial and temporal information of these traffic violations in a geographical map using the standard Google Earth tool. The test-bed is mainly composed of two parts: a computer vision subsystem for traffic sign detection and recognition which operates during both day and nighttime, and an event data recorder (EDR) for recording data related to some specific traffic violations. The paper covers firstly the description of the hardware architecture and then presents the policies used for handling traffic violations

An Intelligent Monitoring System of Vehicles on Highway Traffic

Vehicle speed monitoring and management of highways is the critical problem of the road in this modern age of growing technology and population. A poor management results in frequent traffic jam, traffic rules violation and fatal road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to address this problem is time-consuming, expensive and tedious. This paper presents an efficient framework to produce a simple, cost efficient and intelligent system for vehicle speed monitoring. The proposed method uses an HD (High Definition) camera mounted on the road side either on a pole or on a traffic signal for recording video frames. On the basis of these frames, a vehicle can be tracked by using radius growing method, and its speed can be calculated by calculating vehicle mask and its displacement in consecutive frames. The method uses pattern recognition, digital image processing and mathematical techniques for vehicle detection, tracking and speed calculation. The validity of the proposed model is proved by testing it on different highways.Comment: 5 page

INTRUSION DETECTION SYSTEM USING DYNAMIC AGENT SELECTION AND CONFIGURATION

Intrusion detection is the process of monitoring the events occurring in a computer system or network and analysing them for signs of possible incidents, which are violations or imminent threats of violation of computer security policies, acceptable use policies, or standard security practices. An intrusion detection system (IDS) monitors network traffic and monitors for suspicious activity and alerts the system or network administrator. It identifies unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. Intrusion detection systems (IDS) are essential components in a secure network environment, allowing for early detection of malicious activities and attacks. By employing information provided by IDS, it is possible to apply appropriate countermeasures and mitigate attacks that would otherwise seriously undermine network security. However, Increasing traffic and the necessity of stateful analysis impose strong computational requirements on network intrusion detection systems (NIDS), and motivate the need of architectures with multiple dynamic sensors. In a context of high traffic with heavy tailed characteristics, static rules for dispatching traffic slices among sensors cause severe imbalance. The current high volumes of network traffic overwhelm most IDS techniques requiring new approaches that are able to handle huge volume of log and packet analysis while still maintaining high throughput. This paper shows that the use of dynamic agents has practical advantages for intrusion detection. Our approach features unsupervised adjustment of its configuration and dynamic adaptation to the changing environment, which improvises the performance of IDS significantly. KEYWORDS—Intrusion Detection System, Agent Based IDS, Dynamic Sensor Selection. I

In Vitro Techniques to Accelerate Flavonoid Synthesis in some Euphorbiaceae Members

Intrusion detection is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of computer security policies, acceptable use policies, or standard security practices. An intrusion detection system (IDS) monitors network traffic and monitors for suspicious activity and alerts the system or network administrator. It identifies unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. Intrusion detection systems (IDS) are essential components in a secure network environment, allowing for early detection of malicious activities and attacks. By employing information provided by IDS, it is possible to apply appropriate countermeasures and mitigate attacks that would otherwise seriously undermine network security. However, current high volumes of network traffic overwhelm most IDS techniques requiring new approaches that are able to handle huge volume of log and packet analysis while still maintaining high throughput. Hadoop, an open-source computing platform of MapReduce and a distributed file system, has become a popular infrastructure for massive data analytics because it facilitates scalable data processing and storage services on a distributed computing system consisting of commodity hardware. The proposed architecture is able to efficiently handle large volumes of collected data and consequent high processing loads using Hadoop, MapReduce and cloud computing infrastructure. The main focus of the paper is to enhance the throughput and scalability of the IDS Log analysi

Detection of Motorcycle Headlights Using YOLOv5 and HSV

"Electronic Traffic Law Enforcement" (ETLE) denotes a mechanism that employs electronic technologies to implement traffic regulations. This commonly entails utilizing a range of electronic apparatuses like cameras, sensors, and automated setups to oversee and uphold traffic protocols, administer fines, and enhance road security. ETLE systems are frequently utilized for identifying and sanctioning infractions like exceeding speed limits, disregarding red lights, and turning off the headlights. In Indonesia, there is currently no dedicated system designed to detect traffic violation, especially regarding vehicle headlights. Therefore, this research was conducted to detect vehicle headlights using digital images. With the results of this study, it will be possible to develop a system capable of classifying whether vehicle headlights are on or off. This research employed the deep learning method in the form of the YOLOv5 model, which achieved an accuracy of 94.12% in detecting vehicle images. Furthermore, the white color extraction method was performed by projecting the RGB space to HSV to detect the Region of Interest (ROI) of the vehicle headlights, achieving an accuracy of 73.76%. The results of this vehicle headlight detection are influenced by factors such as lighting, image capture angle, and vehicle type