REAL-TIME TRAFFIC VEHICLE DETECTION AND CLASSIFICATION SYSTEM

Abstract

The rapid growth of urban populations has resulted in a significant increase in the number of vehicles on roads, creating challenges related to traffic congestion, road safety, and transportation management. Traditional traffic monitoring systems often depend on manual observation or basic sensors that provide limited information about individual vehicles. To address these challenges, the Real-Time Traffic Vehicle Detection and Classification System is proposed as an intelligent computer-visionbased solution. The system automatically detects vehicles from live traffic video streams and classifies them into different categories such as cars, buses, trucks, motorcycles, and other road vehicles. The proposed system uses deep-learning-based object detection techniques to identify vehicles accurately from video frames. A trained object detection model processes each frame and determines the location and category of detected vehicles using bounding boxes and confidence scores. Image-processing techniques are applied to improve the quality of input frames and support reliable detection under different traffic conditions. The system can process video obtained from surveillance cameras, traffic cameras, webcams, or prerecorded traffic footage. In addition to vehicle detection, the system performs vehicle classification to determine the type of each detected vehicle. This information can be used to calculate traffic density, monitor vehicle distribution, and analyze traffic patterns. The system can also maintain vehicle counts and generate statistical information about different vehicle categories. Real-time processing enables traffic authorities to obtain immediate information about current road conditions without depending entirely on manual monitoring. The system is designed to support intelligent transportation applications such as congestion monitoring, traffic flow analysis, road surveillance, and traffic management. The collected information can be displayed through a dashboard containing live video, vehicle counts, classification results, and traffic statistics. Alerts can also be generated when traffic density exceeds predefined thresholds or when unusual traffic conditions are identified.