Traffic Violation Detection Research Articles

AI-Enhanced Automatic Traffic Violation Detection and Challan Issuance using GNSS and OBU Sensors in Self-Driving Cars.

The swift progress in self-driving vehicle technology requires strong systems to guarantee adherence to traffic regulations. This study introduces an AI-powered system for automatic detection of traffic violations and issuance of fines in self-driving vehicles utilizing Global Navigation Satellite System (GNSS) and On-Board Unit (OBU) sensors. The system combines AI algorithms with GNSS for accurate geolocation tracking and OBU sensors for monitoring vehicle status. Infractions like running signals, exceeding speed limits, and improper use of lanes are identified instantly. A main server analyzes violation data and automatically generates electronic fines for offenders. The suggested system improves adherence to traffic regulations, minimizes human involvement, and fosters safer roadways by utilizing AI's predictive and analytical features. This novel strategy tackles shortcomings in current traffic enforcement techniques, rendering it a scalable answer for contemporary smart cities. Keywords - Autonomous Vehicles, Traffic Violation Detection, GNSS, OBU Sensors, AI, Smart Cities, Electronic Challan, Traffic Compliance

Traffic Violation Detection Using Computer Vision Techniques

The increasing number of road accidents is still a global concern. Traditional approaches to detecting traffic violators on the road, such as radar guns and sensors, are expensive and time-consuming to maintain and install. This often results in inefficient and ineffective detection of traffic violators. This paper proposes a more cost-effective and efficient approach to traffic violation detection utilizing visual data from CCTV footage. Specifically, the method targets two common violations: crossing red lights and overtaking on double lines. In this study, YOLO is integrated for road object detection, providing the detection of vehicles and traffic lights on the road for our system. Then, the Deep SORT tracker tracks detected vehicles, ensuring continuous monitoring over time. An automated lane detection technique is formulated to identify the stopping line/lane for red light violation detection, enabling precise detection of vehicles that cross the stop lane during red light. For overtaking detection, the system detects the double line to serve as the boundary that vehicles should not cross, identifying illegal overtaking. Furthermore, point-line distance calculation is utilized to detect traffic violators by analyzing their tracked trajectories and positions. The proposed solution is evaluated using real-world CCTV footage from online repositories to reflect the real-world scenarios as closely as possible. Experimental results show that the proposed techniques achieve promising detection of real-time traffic violators, which leads to a safer environment for road users.

Automatic Detection of Traffic Violations Using Yolo Model and Challan Generation

Automatic Detection of Traffic Violations Using Yolo Model and Challan Generation

EFFECTIVENESS ENHANCEMENT OF TRAFFIC CAMERAS BASED ON VEHICLES' AVERAGE SPEED CALCULATION

The analysis of calculating the vehicle speed between two automatic speed cameras within the framework of traffic violation detection demonstrates that using the average speed metric enhances speed limit compliance accuracy. This method reduces the possibility of evading responsibility for violations through temporary speed reduction before cameras. These systems employ usage of existing road cameras infrastructure to determine the minimal travel path of vehicle passing two cameras placed at a certain distance apart to measure the average speed of travel. Technical aspects of implementing the proposed system include utilizing cloud computing for data processing and storage, which reduces infrastructure costs. The system employs the possibility of usage of machine learning algorithms to predict travel time between cameras, considering various factors such as road conditions and speed limits. This approach ensures high calculation accuracy and reduces the number of false violation alerts. To further reduce the possibility of false violations, it is suggested to only use speed limitations based on the traffic signs on the road intervals of the calculated minimal path between two cameras that the vehicle has passed. Implementing the system requires the development of new software for data analysis and integration with existing traffic control systems. This includes designing algorithms to determine the minimum required travel time between cameras and calculating the average speed based on these data. Additionally, methods must be developed to detect and handle exceptions, such as road stops, or route changes, which can affect calculation accuracy. The feasibility of practical implementation of the system was investigated based on the existing infrastructure of traffic cameras in the city of Lviv. Possible minimal routes between cameras were analyzed and compared to the avarage passing speed of vehicles of different categories. The modulated results indicate that such a system would be an effective addition to the traffic control system, while requiring relatively low financial and engineering efforts.

Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3

The application of an electronic violation detection system has begun to be implemented in many countries using CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in the form of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat belts, the use of cell phones while driving, which is influenced by the number of vehicles, vehicle speed and lighting, which can increase the difficulty in the detection process. This research developed a traffic violation detection system using a hybrid model, namely the CNN and LSTM algorithms for the application of discipline using seat belts. The dataset was obtained from RoboFlow Universe with a total of 199 front view car images consists of 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while the LSTM algorithm plays a role in the prediction process. Additionally, the performance evaluation of the CNN+LSTM algorithm will be measured using the accuracy value to measure the performance of the training process and testing process. When measuring the performance of the training process, it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, Yolo3, Yolo3+LSTM. The test results show that Yolo3+LSTM has a higher accuracy compared to the others, at 89%. Next, in the testing process, the CNN+LSTM model will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models have a higher accuracy of 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85%.

Traffic Violation Detection System

Travel convenience is impacted by a number of factors, including the condition of the road, traffic, length of trip, accidents, speed, etc. The daily increase in accident rates poses the biggest concern in Sri Lanka. These events not only result in fatalities but also increase the nation's financial losses. Traffic congestion is brought on by road users' lack of discipline and sentiments, which may result in traffic offenses. The goals of this research are to curb any traffic infractions as well as reduce corruption and nepotism on the road. This system was developed as separate three models namely edge server, fine management system and centralized server. Edge server developed in python is to detect traffic violations such as parking, single line cutting, double line cutting, one way and u turn in real time according to the changing rules and constraints configured by the traffic police admin via the fine management system which supports a configurable platform designed using canvas designing tools. Upon edger server identifying violations, the functions in the centralized server responsible to recognize the number plate of the violated vehicle will be triggered and send a sms to driver for further fine management and payment supported by the fine management system. The mobile application controlling server is responsible to track and inform the violations to nearest police officer in any case the system could not rectify the vehicle number plate.

Traffic Rules Violation Detection using Dashcam Footage

Traffic Rules Violation Detection using Dashcam Video Footage offers a novel strategy for improving traffic management and road safety through automated violation identification and reporting. The project's goal is to create a video processing system that can use dashcam data to identify and classify frequent traffic infractions. The system can recognize traffic infractions including speeding, lane changes without signaling, helmet recognition, and instances of triple riding on two-wheelers with high accuracy thanks to its integration of YOLO, OpenCV and RESNET50 classifier. The project also includes functionality for user account management and the implementation of a secure user authentication system. Users can examine and validate complete violation reports generated by the system prior to submission. The outcomes show a low false positive rate, rapid real-time processing, and good detection accuracy. Law enforcement organizations have shown. This initiative is an example of how important technical innovation is to solving societal issues and advancing public safety. Key Words: YOLO (You Only Look Once), OpenCV, ResNet50 (Residual Neural Network), Dashcam Video Footage.

Traffic Signal Violation Detection System Using Computer Vision

Traffic Signal Violation Detection System Using Computer Vision

Traffic Violation Detection System on Two-Wheel Vehicles Using Convolutional Neural Network Method

The number of vehicles is increasing every year, and along with it, the number of traffic violations is also rising.. Traffic violations are one of the causes of traffic accidents. Currently, traffic violation detection still uses conventional methods, involving the police to take action. Preliminary research on traffic violation detection by several researchers mostly uses the Yolo Method. The study aims to design a traffic violation detection system for two-wheeled vehicles using the Convolutional Neural Network (CNN). In this research, the CNN method was used with the Faster RCNN architecture. Faster R-CNN is composed of convolution layers, Relu, and pooling layers which are used to extract features from images. An image in the size of 3264 x 1836 pixels, with the type of marking violation and helmet use was used as a sample. The number of images used was 660 images with 600 images for training and 60 images for testing. The system will detect traffic violations on two-wheeled vehicles, namely helmet use violations and road marking violations. This traffic violation detection system for two-wheeled vehicles produces the highest accuracy, namely 85% with a maxpooling kernel size value of 1x1, stride 1 and a learning rate of 0.003. This research has the potential to be applied to areas that are less accessible to the police, because the system will record and analyze violations.

Traffic Rules Violation Detection System

Abstract: The paper presents a system that uses surveillance videos to automatically detect bike riders without helmets by us-ing object segmentation and background subtraction techniques. A consolidation approach is introduced to improve accuracy, and three feature representations are compared. The system achieves a detection accuracy of 93.80 percentage and has an average processing time of 11.58 milliseconds, making it a cost-effective and real-time solution for managing traffic violations related to helmet usage

APPLYING MACHINE LEARNING AND OBJECT DETECTION FORIDENTIFYING TRAFFIC RULE VIOLATION: OVER SPEEDING

We have observed that traffic offences are manually inspected by the traffic department in many developing nations, such as India. Such systems enable traffic management and traffic law enforcement. Following this, the present study focuses on Traffic violations as one of the key issues hindering the efficient operations and safety aspects in the urban traffic area. Currently, traffic violations are controlled using enforcement measure that demands a high manual workforce, and resource which entails high economic cost. Contrary, the use of automatic traffic violation detection systems in many of the urban cities of the developed and developing countries is being implemented for a higher degree of traffic enforcement and adherence to the traffic operation rules. Following this the present study focus on the applicationof machine learning for automatic traffic violation detection for weak- lane disciplined mixed traffic conditions. The resulting exhibit that the developed model can detect traffic violations of over-speeding at an accuracy of 95%. The traditional methods like speed camera use the basic principle of the Doppler Effect and RADAR technologies but at present,Due to their drawbacks which vary from the expense of purchasing equipment, increase in work and labour costs, and lack of monitoring, they are ineffective. As a result, it is a straightforward, affordable, and practical speed detecting method in Mixed traffic conditions. So here, we prefer automatic speed violation detection. The suggested solution will be highly accurate and cost-efficient.

Detection of Traffic Rule Violations Using Machine Learning: An Analytical Review

This research paper focuses on current and previous efforts to detect traffic rule violations. So far, some remarkable works have been discovered, and many approaches for detecting traffic rule violations have been introduced from the current situation. Hence, machine learning has been the main target to detect traffic rule violations. A summary of the frameworks and methods that have been used to solve this problem so far is also provided in this study. This study has been divided into two parts. In the first part, the recent works on traffic rule violations have been portrayed. Moreover, the algorithms and frameworks that have been used so far and major works on violation detection using machine learning can be found in this section. In the second part, this study summarizes a brief discussion based on the image quality, camera resolution, device performance, and accuracy level of the works, as well as the algorithms and frameworks that have been used to conduct the detection of traffic rule violation problems using machine learning.

Traffic Signal Violation Detection System

Traffic Signal Violation Detection System

Prospects for the Application and Development of Computer vision Technologies

The paper presents examples from different fields of human activity in which computer vision technology is used. It is analyzed what is machine vision, how it differs from computer vision. The advantages and disadvantages of the technology are touched upon, an overview of the system components and methods for processing machine vision is presented, and the areas in which it is applied are explored. The study shows how relevant and in demand computer vision is, as well as the ways of its progress in the near future. The issues of application of machine vision systems in monitoring the implementation of safety protocols, in quality management are touched upon. Here, machine vision is implemented as an automated visual control and quality control of production processes. The application of computer vision systems in transport for the classification of vehicles, their counting, traffic analysis, detection of traffic violations: speeding, running a red light, not allowing a pedestrian is considered. This improves road safety. Computer vision is used to detect distracted driving: drowsiness, daydreaming, distraction; up to the determination of the sobriety of the driver. The use of computer vision to monitor order in large cities is affected by determining the deviant behavior of citizens.

Traffic Violation Detection and Control

The purpose of this paper is to understand the role & requirement of automation in the traffic management domain. With number of vehicles increasing daily & India being the nation with largest number of 2-wheelers, its Important to track vehicles at an increasing pace than the convention systems in place. We aim to use a system using CV (Computer Vision) & YOLOv5 object detection for classifying vehicles in different categories, considering the vast amount of travel modes used in the Indian Peninsula like Autos & 2-wheelers.

Automatic Traffic Red-Light Violation Detection Using AI

Our research is the design of a traffic signal violation detection system using machine learning that learns to prevent the increasing number of road accidents. The system is optimized in terms of accuracy by using the region of interest and location of the vehicle with a red-signal state. By modifying some parameters in the YOLOV5s and re-training the COCO dataset, we can create a model which can be predicted with a high accuracy of 82% for vehicle identification, 90% for traffic signal status change and up to 86% for violation detection. This can be used for red light violation detection which will help the traffic police on traffic management.

IoT Based Signal Violated Vehicle Detector

Abstract: The number of new vehicles on the road is increasing rapidly, which in turn causes highly congested roads and due to the long awaiting red signals, few vehicle drivers ignore traffic rules by violating traffic signals. This leads to a high number of road accidents and hence it is essential to detect the signal violated vehicles automatically. Traffic violation detection systems using IOT technology can able to send violated vehicle information to the concern traffic police station automatically by which the vehicle can be penalized immediately. The proposed system can be implemented using vehicle ID card installed in the vehicle itself such that if the vehicle moves further during presence of red signal, automatically, vehicle information in the form of vehicle registration number, type of vehicle (weather it is two-wheeler, car, truck, auto, etc.), owner name, vehicle colour, etc, can be forwarded to the concern authorities through concern mobile phones. To prove the concept practically, all vehicles must be equipped with these low-cost wireless ID cards such that the vehicle data can be transmitted continuously. The demo module contains a mini automatic traffic signal post along with simulated road. The system is designed such that when red signal is energized and during this period, if any vehicle crosses the zebra lines, the system receives the vehicle data and transmits the same through Wi-Fi module. If anybody violates the traffic signal, immediately alarm will be energized to alert the nearest traffic police. The main processor must be installed near the traffic signals, which regrets to acquire vehicle information during the presence of yellow and green signals. The demo module contains one toy car which will be equipped with its wireless ID card. Major building blocks: Simulation of single lane road constructed with traffic signal post & zebra crossing, main processor designed with Arduino MCU, wireless ID card designed with 89C2051 controller chip, IR signal decoder (TSOP1738), Wi – Fi module, Alarm, IR LED, 5v Power supply unit, Toy car, etc.

Traffic violation detection and penalty generation system using web server

Using Radio Frequency Identification (RFID) technology, this work aims to create an intelligent traffic system. It is developed and operated in such a way that fines are tracked independently. RFID technology, passive tags, a processing unit (computer), and a communication system were all utilized with low-cost equipment. The first level is represented by data collection via RFID, which scans any identifiable tag connected to cars. After that, information is received by server on internet connection. Modern era is usually growing with an aim to make people's each day obligations easier. One of the major issues facing developing cities is traffic management, which is exacerbated by increasing population density and vehicle numbers without expanding roads. Road congestion wastes time and fuel, resulting in increased pollution. This observe demonstrates that growing a traffic management system with the use of the Internet of Things (IoT) is achievable and inexpensive.

Neural Network Based Smart City Application for Traffic Violation Detection

The main application of helmet detection is in traffic roads where accidents are more. Even though various measures are taken by government, it is not followed correctly by the motorcyclists, so several smart techniques should be employed. In developing countries like India, the two-wheeler is the most common means of transportation. Though it is convenient to ride, negligence of Helmet compulsion law by the riders is leading to many accidents. According to the statistics provided in the Road Accident Report, at least 98 riders, who were not wearing a helmet, died daily in 2017. Deaths caused by not wearing helmet rose to 36,000 in 2017 which was 10,135 in 2016. WHO has declared that the negligence of riders in using safety devices as one of the causes for the rise in road accidents. Government has implemented many methods to catch the violators. But those methods require human assistance, which decreases the performance and reliability of the system. In this project, an automatic helmet detection system is designed using Faster RCNN machine learning algorithm. Faster RCNN algorithm is used to detect the helmet.

RANCANG BANGUN PROTOTYPE SISTEM PENDETEKSI PELANGGARAN PADA ZEBRA CROSS DI LAMPU LALU LINTAS BERBASIS ARDUINO

Current road users cannot be separated from the number of violators, therefore traffic lights are made to regulate traffic on the road. At traffic lights, there is also a zebra crossing which serves as a means of crossing the road for pedestrians. To minimize violations at road intersections, researchers designed a tool to detect traffic violations.
 Traffic violation detection tool is made in prototype form using a control system with Arduino nano and software. This traffic light system uses LDR and laser sensors to detect these violations by cutting the laser which sends light to the LDR. This tool is also equipped with a webcam camera that functions to photograph violations that occur and a buzzer that functions as a warning to officers and riders in the event of a violation with an average response speed of the webcam of 2.37 seconds and the average response speed of the buzzer is 0.4 seconds. . The snapshot from the webcam is saved automatically on your PC / Laptop.