Vehicle Detection, Tracking, and Counting System with Number Plate Recognition in a Petrol Station

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dc.contributor.advisorDr. Shazzad Hosain
dc.contributor.authorShadman Sakib
dc.contributor.authorRahul Deb Roy
dc.contributor.authorMoriom Islam Mou
dc.contributor.id1931024042
dc.contributor.id1931132042
dc.contributor.id1931333042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-06-27
dc.date.accessioned2025-06-26T04:40:42Z
dc.date.available2025-06-26T04:40:42Z
dc.date.issued2023
dc.description.abstractThe efficient management of vehicles within a petrol station is a critical aspect of ensuring smooth operations, security, and customer service. In this technological age, the integration of vehicle tracking and number plate recognition systems has become imperative. The primary objective of this system is to provide real-time monitoring of vehicle entry and exit, enabling the station management to accurately track the number of vehicles on the premises at any given time. The system utilizes cameras equipped with license plate recognition software to capture and recognize vehicle number plates as vehicles enter and exit the station. Petrol stations, at the heart of transportation networks, serve as vital nodes for the distribution of fuel and energy resources. Efficiently managing the flow of vehicles through these stations is essential for ensuring smooth operations, enhancing security, and optimizing customer service. After conducting extensive market research, we identified a significant gap in the application of vehicle tracking, counting, and classification technology at petrol stations. Our analysis revealed that this technology had not been effectively implemented in the real world, despite its potential to bring substantial benefits to the industry. The intricate process of detecting, tracking, Bangla Number plate detection and tallying vehicle types within a CCTV surveillance system is an inherently sophisticated yet remarkably efficient operation. For the subsequent stage of vehicle counting, we harnessed the capabilities of Python, the Ultralights library, and OpenCV. Moreover, the datasets are collected from Kaggle and Roboflow for training purposes.
dc.description.degreeUndergraduate
dc.identifier.cd600000168
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1187
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSU library
dc.titleVehicle Detection, Tracking, and Counting System with Number Plate Recognition in a Petrol Station
oaire.citation.endPage46
oaire.citation.startPage1
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