Vehicle Detection and Tracking in Real-time using YOLOv4-tiny

Aissa Amrouche, Youssouf Bentrcia, Ahcène Abed, Nabil Hezil

202221 citationsDOI

Abstract

Vehicle detection and tracking is a popular research topic in Intelligent Transportation Systems. The goal of this paper is to detect, identify, and track vehicles in surveillance camera footage in order for them to be extracted efficiently and accurately. You Only Look Once version 4 (YOLOv4) algorithm is used in this paper to propose a real-time vehicle detection and tracking system. The suggested system has been evaluated using a variety of measures, including accuracy, precision, and recognition recall. For the experimental data, the system attained an accuracy of 96.30 percent and an overall accuracy of 94.17 percent. The results reveal that the suggested system successfully tracks vehicles in the scene.

Topics & Concepts

Computer scienceVehicle tracking systemArtificial intelligenceTracking (education)Precision and recallIntelligent transportation systemTracking systemTrack (disk drive)Computer visionReal-time computingObject detectionPattern recognition (psychology)Kalman filterEngineeringOperating systemPsychologyCivil engineeringPedagogyAdvanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsVehicle License Plate Recognition