Vehicle Classification and Tracking Based on Deep Learning

Authors

  • Hyochang Ahn Wonkwang University, Jeonbuk, Iksan City 54538, Republic of Korea
  • Yong-Hwan Lee Wonkwang University, Jeonbuk, Iksan City 54538, Republic of Korea

DOI:

https://doi.org/10.13052/jwe1540-9589.21412

Keywords:

Vehicle classification, Moving object tracking, Deep learning, YOLO

Abstract

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.

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Author Biographies

Hyochang Ahn, Wonkwang University, Jeonbuk, Iksan City 54538, Republic of Korea

Hyochang Ahn received the M.S. degree and Ph.D. in Electronics and Computer Engineering from Dankook University, South Korea, in 2006 and 2012, respectively. He was a Research Professor at Dankook University, South Korea, from 2014 to 2016. Currently, he is working as research director in R&D at Innogru, Korea. His research interests include Image Processing, Computer Vision, Embedded system and Mobile Programming.

Yong-Hwan Lee, Wonkwang University, Jeonbuk, Iksan City 54538, Republic of Korea

Yong-Hwan Lee received the MS degree in computer science and PhD in electronics and computer engineering from Dankook University, Korea, in 1995 and 2007, respectively. He is an active member of International Standard committees of ISO/IEC JTC1 SC29 responsible for Image Retrieval and Coding issues. Currently, he is a Professor at the Department of Digital Contents, Wonkwang University, Korea. His research areas include Image Retrieval, Image Coding, Computer Vision and Pattern Recognition, Augmented Reality, Mobile Programming and Multimedia Communication.

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Published

2022-04-20

Issue

Section

SPECIAL ISSUE ON Future Multimedia Contents and Technology on Web in the 5G Era