International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | To Design an AI-Powered Traffic Rule Violation Detection Framework Using Computer Vision |
|---|---|
| ABSTRACT | Traffic rule violations such as helmetless riding, signal jumping, wrong-way driving, and lane violations significantly contribute to road accidents and congestion. This paper proposes an AI-powered traffic rule violation detection framework using computer vision for real-time monitoring and automated evidence generation. The system captures video from roadside or CCTV cameras and performs frame-wise preprocessing followed by object detection to identify vehicles, riders, helmets, and traffic signals. Violations are detected using rule-based logic and tracked across frames to reduce false detections. Number plate extraction is optionally performed for documentation and reporting. The framework is implemented using Python and OpenCV, with support from deep learning models such as YOLO for object detection. The proposed approach improves enforcement efficiency, reduces manual surveillance efforts, and enables scalable deployment in smart city environments. Experimental evaluation shows that the system can accurately detect common violations under varying traffic conditions, making it suitable for real-time traffic monitoring and automated rule enforcement. |
| AUTHOR | A.ELANGO, SUGANDHI MARIYAL D, MURUGAN B, CHANDRU Y, KRISHNAN S Assistant Professor, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India UG Scholar, Department of Computer Science and Engineering, P.S.V College of Engineering and Technology, Mittapalli, Krishnagiri, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404063 |
| pdf/63_To Design an AI-Powered Traffic Rule Violation Detection Framework Using Computer Vision.pdf | |
| KEYWORDS | |
| References | 1. Zhang, S., et al. (2018). Automatic License Plate Recognition Using Deep Learning.This study presented deep learning-based license plate detection and recognition. The system performed well in complex traffic scenes. It supports automated evidence generation. 2. Chaudhary, P., & Sharma, V. (2019). Traffic Signal Violation Detection System Using Video Analytics.This paper proposed automated detection of red-light violations. It reduced manual monitoring efforts. The study highlights the importance of real-time analysis. 3. Reddy, K., et al. (2020). Wrong-Way Vehicle Detection Using Deep Learning.The study developed a system for identifying vehicles moving in the wrong direction. It enhanced road safety measures. The research is relevant to intelligent traffic systems. 4. Kumar, A., & Singh, D. (2021). Lane Detection and Monitoring Using Computer Vision.This research introduced lane detection using image processing methods. It improved lane discipline monitoring. The findings support automated lane violation detection. |
