Aug 2026· Asian Journal of Research in Computer Science· 0 citations
TL;DR
The developed and implementation of an AI-based traffic violation detection system designed to automate the monitoring of road traffic violations using CCTV footage and computer vision techniques provides a practical and innovative solution to contemporary traffic violation control and management challenges.
Abstract
Aims: This project presents the development and implementation of an AI-based traffic violation detection system designed to automate the monitoring of road traffic violations using CCTV footage and computer vision techniques. The aim is to create a comprehensive solution that can analyse real-time or recorded video streams to detect violations such as red-light running and speeding, thereby improving road safety and reducing the manual surveillance burden.
Study Design: The research employed a modular, data-driven approach combining software engineering best practices with advanced machine learning and computer vision techniques. The system was developed iteratively with a component-based architecture supporting separation of concerns and scalability.
Place and Duration of Study: The study was conducted at Babcock University, Department of Computer Science, School of Computing, Ilishan-Remo, Nigeria, from September 2023 to May 2024.
Methodology: The implementation utilised Python as the primary programming language, OpenCV for image processing and video analysis, and the YOLOv8 deep learning model for real-time object detection and vehicle classification. Multi-object tracking was accomplished using the DeepSORT algorithm, enabling consistent vehicle identification across video frames. Speed estimation was performed through frame displacement analysis and timestamp calculations. A rule-based violation detection module was integrated to identify traffic offences, including red-light violations and speeding. An Automatic Number Plate Recognition (ANPR) component utilising Optical Character Recognition (OCR) was included for vehicle identification. A structured MySQL database was implemented to store violation records with timestamps and evidence. An administrative dashboard, developed using HTML, CSS, JavaScript, and Bootstrap, provides real-time analytics, processing history, and violation reporting capabilities. The system architecture follows a three-tier model: Client-Side (Frontend), Server-Side (Backend), and Database Layer.
Results: Functional testing confirmed successful video upload, accurate vehicle detection using the YOLOv8 model, which demonstrated high precision, reliable multi-object tracking with consistent vehicle ID maintenance, accurate speed estimation through frame analysis, and correct violation flagging with real-time visual alerts. Performance evaluation demonstrated that the YOLOv8 model processed video frames efficiently at optimal detection speed, maintaining consistent detection accuracy and tracking reliability under various traffic conditions, including different lighting, weather, and traffic density scenarios. The system successfully generated violation reports with vehicle identification through licence plate recognition, timestamps, and evidence imagery for enforcement purposes.
Conclusion: The AI-based traffic violation detection system provides a practical and innovative solution to contemporary traffic violation control and management challenges. By integrating advanced computer vision features, including object detection, multi-object tracking, and automatic number plate recognition, the system analyses captured footage with high accuracy to identify violations and extract relevant vehicle details. The system's ability to provide real-time evidence capture and generate automated violation reports demonstrates how modern AI technologies can be effectively leveraged to create practical tools that meet contemporary traffic management needs, enabling authorities to manage road safety with greater effectiveness and efficiency.
Road accidents are a major cause of injuries, fatalities, and traffic disruptions worldwide. Timely detection of vehicle accidents is critical for providing quick emergency response and reducing the impact of such incidents. This project presents an AI-powered real-time Vehicle Accident Detection system developed using Python, OpenCV, and Deep Learning techniques. The system analyzes live video streams or recorded footage to automatically detect and classify vehicle accidents.The proposed approach uses computer vision methods through OpenCV to process video frames and extract meaningful visual information. A Convolutional Neural Network (CNN) is trained on image and video datasets containing accident and non-accident scenarios. The CNN model learns spatial features such as vehicle movement, collision patterns, and sudden changes in motion to accurately identify accident events. Once an accident is detected, the system can generate automatic alerts to notify concerned authorities or emergency services.This project demonstrates an end-to-end implementation, starting from dataset preparation and model training to real-time deployment. The system improves accident detection accuracy compared to traditional methods and reduces dependency on manual monitoring. Overall, this AI-based accident detection system highlights the effective use of deep learning and computer vision technologies to enhance road safety and support faster emergency response mechanisms
Unknown authors· International Journal of Sci...· 0 citations
Vehicle recognition is a critical component of traffic analysis and the progress of advanced transportation systems, underscoring the importance of automated, real-time methods that reduce the need for manual observation. While the field has seen notable innovations in deep learning-centric detection technologies, many of these approaches require considerable computational strength and are not well-suited for real-time application in resource-constrained environments. In response to this limitation, the present study introduces a streamlined vehicle detection framework that combines background subtraction for motion-oriented foreground extraction with a Haar cascade classifier for object identification in traffic video sequences. The system is evaluated using real-world highway traffic recordings under different illumination conditions, including both day and night scenarios. The experiment's findings show that the system achieves an overall accuracy of 82.08%, with a precision of 85.33%, a recall of 66.67%, and an F1-score of 74.86%. The system also demonstrates consistent performance across different lighting conditions. These findings indicate a trade-off between detection accuracy and computational efficiency, where the proposed approach prioritizes practical deployment feasibility. Overall, the results suggest that classical computer vision techniques remain viable alternatives for real-time traffic monitoring in environments with limited computational resources.
Ni Gusti Ayu Dasriani, Anthony Anggrawan, Khasnur Hidjah et al.· International Journal of Inf...· 0 citations
Motorcycle traffic accidents have been on the rise because of low compliance with helmet laws and there is need for automated traffic monitoring and enforcement systems. This paper proposes a traffic enforcement solution that combines YOLOv8 based object detection and PaddleOCR for automatic violation detection and license plate recognition of motorcyclists. The system will detect motorcycles, riders, helmets and license plates from traffic images, identify the helmet violation cases by analyzing their spatial relationship and extract the registration numbers of the vehicles for automatic enforcement. The solution is trained and tested using 4,169 traffic images that have been labeled. The helmet detection model obtains an mAP@50 score of 0.983, and the license plate detection module gives localization of the license plates which makes them recognizable by the OCR. There is a Flask based web app that allows users to upload images, detect violations, generate evidence, and notify fines.
With the increasing number of vehicles, urbanization and the constant rise in road usage, traffic violations have become hugely problematic in today's transportation situations. Some of the most common dangerous driving behaviors that lead to road accidents and traffic delays are as follows: Not wearing a helmet, running a red light, and breaking lanes, breaking seatbelt, using a cell phone while driving and triple riding. Maintaining consistent observation, precise detection, scalability, and speedy identification of traffic infractions in complex road situations are all challenges faced by current traffic violation monitoring methods. Factors such as high traffic levels, uneven lighting, environmental interference, and requiring human supervision limit the effectiveness of current monitoring methods. Therefore, it becomes essential to have a sophisticated automated system that can efficiently do real-time traffic infraction analysis. The proposed study utilizes a novel Traffic violations identification method based on YOLOv8 to detect many traffic violations in the surveillance photos and videos. To achieve the system execution, a Traffic Rule-net Dataset was developed from a set of traffic data collected from different scenarios of city transport, highways and crossroads. The quality of the features and the robustness of the suggested model were enhanced using a number of data pre-processing techniques, including normalization, image size modification, data augmentation, and filtering. The new framework was applicable in all environmental conditions, allowing for efficient object localization and classification of different breaches. From the experimental evaluation it is clear that there was a reduction in false detection, performance, and detection efficiency. Infrancements of the traffic rules may be easily and efficiently detected for traffic control through the use of intelligent monitoring.
Selvam L, G. Aninthitha, P. M et al.· 2026 7th International Confe...· 0 citations
Rapid urbanization and increasing vehicle numbers have led to a significant rise in road accidents, posing serious threats to human life and economic stability. Traditional accident detection systems rely on manual reporting, causing delays in emergency response. To address this, computer vision integrated with intelligent transportation systems offers an effective solution. This study analyzes pre-2018 approaches to traffic accident detection using video surveillance. The proposed system automatically detects accidents by analyzing traffic camera footage through feature extraction, motion analysis, and pattern recognition. It identifies abnormal vehicle behavior such as sudden speed drops, collisions, and irregular trajectories. Classical computer vision techniques like optical flow, background subtraction, edge detection, and machine learning methods such as Support Vector Machines (SVM) and decision trees are used. The system includes preprocessing, object detection, trajectory tracking, and classification of normal and abnormal events, supported by mathematical modeling and threshold-based decisions. Results show high detection accuracy with low false alarms, especially in controlled environments like highways and intersections. However, challenges remain in real-time implementation due to lighting, occlusion, and camera angle issues. Future work suggests incorporating deep learning and multi-sensor data fusion to improve performance. In conclusion, computer vision-based accident detection is a promising approach to enhancing road safety and reducing response time, contributing to the advancement of smart transportation systems.
Nimal Perera, Tharindu Jayasinghe· International Journal of Mod...· 0 citations
The high rate of growth in road traffic has been a major cause of drastic increase in the number of accidents which have resulted in high demand of effective real-time monitoring and response systems. This paper introduces a hybrid system of accident detection and classification of severity based on CCTV video streams implemented on deep learning. The suggested system is a combination of the YOLOv8s object detector model and a Convolutional Neural Network (CNN) using MobileNetV3 to guarantee the efficient performance of the system. YOLOv8s is used to identify regions of accidents in the continuous video frames and the CNN model is used to categorize the identified accidents into moderate and severe. The system is trained on a publicly available dataset of more than 15,000 annotated images, which were recorded in a variety of environmental conditions, which guarantees a robust system and generalization. Experimental findings indicate that it has a high detection accuracy with good precision, recall and mAP values and stable classification accuracy. Also, an automated alert system is included to inform emergency services in case of severe accidents identified so that they can respond quicker and have a lesser effect. The suggested framework is minimal and scalable and appropriate to be implemented in intelligent transportation systems and smart city applications.
J. Viswanath, Jyothinagaram Harika, Gajulapalli Karishma et al.· 2026 4th International Confe...· 0 citations
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