Vehicle Classification and Tracking Based on Deep Learning


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



Vehicle classification, Moving object tracking, Deep learning, YOLO


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.


Tian, B., Morris, B. T., Tang M. et al., “Hierarchical and Networked Vehicle Surveillance in ITS: A Survey”, IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 557–580, 2014.

Han, D., Cooper, D. B., and Hahn, H. S., “Bayesian Vehicle Class Recognition using 3-D Probe”, International Journal of Automotive Technology, vol. 14, no. 5, pp. 747–756, 2013.

Ahn, H., and Lee, Y. H., “Performance Analysis of Object Recognition and Tracking for the Use of Surveillance System”, Journal of Ambient Intelligence and Humanized Computing, vol. 7, no. 5, pp. 673–679, 2016.

Li, Q. L., and He, J. F., “Vehicles Detection based on Three-Frame-Difference Method and Cross-Entropy Threshold Method”, Computer Engineering, vol. 37, no. 4, pp. 172–174, 2011.

Munroe, D. T., and Madden, M. G., “Multi-Class and Single-Class Classification Approaches to Vehicle Model Recognition from Images”, Proceedings of the 16th Irish Conference on Artificial Intelligence and Cognitive Science, pp. 1–11, 2005.

Morris, B., and Trivedi, M., (2006, November). “Improved vehicle classification in long traffic video by cooperating tracker and classifier modules. In 2006 IEEE International Conference on Video and Signal Based Surveillance (pp. 9–11). IEEE.

Prahara, A., “Car Detection based on Road Direction on Traffic Surveillance Image”, International Conference on Science in Information Technology, pp. 344–349, 2016.

Sakai, Y., Oda, T., Ikeda, M., and Barolli, L., “An Object Tracking System based on SIFT and SURF Feature Extraction Methods”, International Conference on Network-Based Information Systems, pp. 561–565, 2015.

Moranduzzo, T., and Melgani, F., “A SIFT-SVM Method for Detecting Cars in UAV Images”, International Geoscience and Remote Sensing Symposium, pp. 6868–6871, 2012.

Sotheeswaran, S., and Ramanan, A., “Front-View Car Detection using Vocabulary Voting and MEAN-SHIFT Search”, International Conference on Advances in ICT for Emerging Regions, pp. 16–20, 2015.

Lou, Z., Jiang, G., Jia, L., and Wu, C., “Monocular 3D Tracking of MEAN-SHIFT with Scale Adaptation based on Projective Geometry”, International Conference on Multimedia Technology, pp. 1–4, 2010.

Prahara, A., “Car Detection based on Road Direction on Traffic Surveillance Image”, International Conference on Science in Information Technology, pp. 344–349, 2016.

Guzman, S., Gomez, A., Diez, G., and Fernández, D. S., “Car Detection Methodology in Outdoor Environment based on Histogram of Oriented Gradient and Support Vector Machine, 2015.

Bougharriou, S., Hamdaoui, F., and Mtibaa, A., “Linear SVM classifier based HOG Car Detection”, International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, pp. 241–245, 2017.

Nie, Y., Sommella, P., O’Nils, M., Liguori, C., and Lundgren, J, “Automatic Detection of Melanoma with YOLO Deep Convolutional Neural Networks”, International Conference on e-Health and Bioengineering, 2019.

Xu, Z., Shi, H., Li, N., Xiang, C., and Zhou, H., “Vehicle Detection Under UAV Based on Optimal Dense YOLO Method”, International Conference on Systems and Informatics, pp. 407–411, 2018.

Lou, L., Zhang, Q., Liu, C., Sheng, M., Zheng, Y., and Liu, X., “Vehicles Detection of Traffic Flow Video Using Deep Learning”, International Conference on Data Driven Control and Learning Systems Conference, pp. 1012–1017, 2019.






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