Vehicle Identification and Counting System Using Machine Learning

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dc.contributor.advisorAtiqur Rahman
dc.contributor.authorMd. Shahrior Gohor
dc.contributor.authorB. M Tanvir Hossain
dc.contributor.id1610824042
dc.contributor.id1611687042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-06-29
dc.date.accessioned2025-06-29T09:37:04Z
dc.date.available2025-06-29T09:37:04Z
dc.date.issued2021
dc.description.abstractIn the realm of road management, the significance of intelligent vehicle recognition and counting has grown immensely. However, this task is made inherently challenging by the wide variety of vehicle sizes and shapes on the road, which directly influences the accuracy of vehicle counting. To tackle this challenge, we present a robust vision-based vehicle recognition and counting system that employs the Yolo machine learning method. In the context of intelligent road traffic management and control, accurate vehicle identification and comprehensive statistics in road monitoring video sequences are paramount. The proliferation of traffic surveillance cameras has resulted in a vast reservoir of traffic video footage for analysis. An elevated viewing angle is necessary to achieve a broader view of the road surface. But at this increased viewing angle, the size of vehicles varies significantly, and the accuracy of detecting smaller objects situated farther from the road diminishes. In light of these formidable challenges posed by diverse camera conditions, addressing and surmount these obstacles effectively is crucial. In this study, we propose a promising approach for vehicle detection that goes beyond mere identification. The findings from our system serve as a foundational component for multi-object tracking and vehicle counting. Our vision-based approach, utilizing the Yolo machine learning method, is tailored to accommodate the dynamic nature of road traffic. By continuously improving the accuracy of vehicle recognition and counting, we contribute to more intelligent road traffic management and control. The insights gained from this system offer valuable data for traffic analysis and decision making, ultimately leading to safer and more efficient roadways.
dc.description.degreeUndergraduate
dc.identifier.cd600000601
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1193
dc.language.isoen
dc.publisherNorth South University
dc.rights© NSU Library
dc.titleVehicle Identification and Counting System Using Machine Learning
dc.typeThesis
oaire.citation.endPage66
oaire.citation.startPage1
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