Traffic Monitoring System

Abstract

Traffic monitoring is one area that utilizes Deep Learning for several purposes. By using cameras installed in some spots on the roads, many tasks such as vehicle counting, vehicle identification, traffic violation monitoring, vehicle speed monitoring, etc. can be realized. In this paper, we discuss a Deep Learning implementation to create a vehicle Classification system by track the vehicles movements. To enhance the system performance and to reduce time in deploying Deep Learning architecture, hence pre-trained model of YOLOv3 is used in this research due to its good performance and moderate computational time in object detection. This research aims to create a vehicle classification system to help human in classify and movement track the vehicles that cross the street. The counting is based on four types of vehicle, i.e. car, motorcycle, bus, and truck, while previous research counts the car only. As the result, my proposed system will capable to count the vehicles crossing the road based on video captured by camera with the highest accuracy of 97%. Keywords: Deep Learning,YOLOv3,Classification