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Title	Cctv Surveillance-Based Vehicle Identification: Query-Driven Search Using Colour, Manufacturer & License Plate
ID_Doc	13518
Authors	Ramakanth Kumar P.; Jambur P.V.; Kumar D.
Year	2025
Published	Proceedings of the 3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025
DOI	http://dx.doi.org/10.1109/IITCEE64140.2025.10915511
Abstract	The paper addresses the challenges of vehicle detection and identification in road safety and surveillance, especially in hit-and-run incidents and vehicle theft cases. The solution combines computer vision with YOLO models for real-time vehicle detection, colour classification, and a custom CNN for accurate vehicle type classification. YOLO is also used for license plate recognition, paired with Tesseract OCR to extract alphanumeric details for precise identification. The colour identification process is two-fold: YOLO detects vehicles in video frames and isolates their bounding boxes, which are then processed through a YOLO-based classifier trained on the VCoR dataset with 15 vehicle colour labels. Trained over 25 epochs, the model achieved 85.6% top-1 accuracy and 99.5%top-5 accuracy, proving reliable across diverse lighting, weather, and traffic conditions. This system automates vehicle detection and identification, reducing the time and manual effort needed to review large video datasets. Integrating vehicle type classification, colour recognition, and license plate identification, it provides a scalable and efficient tool for law enforcement and surveil- lance, enabling rapid suspect vehicle identification in complex traffic environments. The solution enhances traffic monitoring and security, delivering high accuracy in real-world scenarios. Scalable across urban areas, highways, toll booths, and smart city infrastructure, it offers a versatile approach to modern traffic surveillance. © 2025 IEEE.
Author Keywords	CNN; colour classification; license plate recognition; OCR; real-time vehicle tracking; smart city infrastructure; traffic surveillance; VCoR dataset; vehicle detection; vehicle identification; YOLO

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