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 | Advanced Vehicle Surveillance System: A Deep Learning Approach for License Plate Recognition and Tracking |
|---|---|
| ABSTRACT | This paper presents a complete framework for a vehicle surveillance system designed for automatic license plate recognition and tracking using deep learning techniques. The proposed system includes a processing pipeline that consists of video processing, object detection, optical character recognition (OCR), and data visualization. For plate detection, we use the YOLOv5 model. We achieve text recognition through a combination of Easy OCR and Tesseract. Additionally, we integrate post-processing algorithms to improve recognition accuracy by enhancing image quality, normalizing text, and validating plate formats. The system includes a web-based interface that allows users to upload surveillance footage, search for detected license plates, and visualize results through an interactive map and analytical dashboards. Experimental results show the system’s performance under real-world conditions with different lighting, camera angles, and vehicle speeds. The proposed approach has strong potential for use in traffic management, law enforcement, and smart city development. |
| AUTHOR | PROF. PALLAVI PATHARE, MAYUR SONAWANE, PRASAD RAKTATE, YASH SONAWANE, ADITYA UGALE |
| PUBLICATION DATE | 2025-11-01 |
| VOLUME | 175 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1310030 |
| pdf/30_Advanced Vehicle Surveillance System A Deep Learning Approach for License Plate Recognition and Tracking.pdf | |
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