Open AccessVehicle Classification and Tracking Based on Deep Learning
Author(s) -
Hyochang Ahn,
Yong-Hwan Lee
Publication year - 2022
Publication title -
journal of web engineering/journal of web engineering on line
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 13
eISSN - 1544-5976
pISSN - 1540-9589
DOI - 10.13052/jwe1540-9589.21412
Subject(s) - computer science , deep learning , tracking (education) , vehicle tracking system , artificial intelligence , track (disk drive) , volume (thermodynamics) , scheme (mathematics) , computer vision , tracking system , transport engineering , engineering , segmentation , kalman filter , psychology , mathematical analysis , pedagogy , physics , mathematics , quantum mechanics , operating system
Traffic volume is gradually increasing due to the development of technology and the concentration of people in cities. As the results, traffic congestion and traffic accidents are becoming social problems. Detecting and tracking a vehicle based on computer vision is a great helpful in providing important information such as identifying road traffic conditions and crime situations. However, vehicle detection and tracking using a camera is affected by environmental factors in which the camera is installed. In this paper, we thus propose a deep learning based on vehicle classification and tracking scheme to classify and track vehicles in a complex and diverse environment. Using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.