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Moving Vehicle Detection for Measuring Traffic Count Using OpenCV

Priya1,Amandeep2,Varsha3, Arjoo4, Kirti5
M.Sc. Computer science1, 3, 4, 5Artificial Intelligence and Data Science

Assistant Professor2   Artificial Intelligence and Data Science
Guru Jambheswar University of Science and Technology, Hisar

Email- priyakaliramna2002@gmail.com

Abstract

This research focuses on the development of a real-time vehicle detection and tracking system using OpenCV and deep learning models to address growing traffic management challenges. The project integrates the Gaussian Mixture-based Background Subtraction (MOG2), morphological operations, contour detection, and tracking methods like Kalman Filtering and Deep SORT. Additionally, object classification via YOLO and MobileNet-SSD significantly improves the accuracy of identifying and distinguishing vehicles. Evaluated on multiple real-world datasets under various environmental conditions, the system demonstrated strong performance with over 91% mAP, 92.5% precision, and robust multi-object tracking accuracy. This study serves as a scalable framework for intelligent traffic surveillance.

Keywords: OpenCV, Vehicle Detection, YOLO, Traffic Monitoring, Deep SORT, Computer Vision, Object Tracking, Motion Detection, Python, Real-time Systems