Machine vison-based system for vehicle classification and counting using YOLO
This paper proposes a machine vision-based system that is used for vehicle classification and counting. Vehicle classifier and counter are a very crucial in road design to determine the road load capacity. It is also important to monitor, manage, and analyze the traffic flow. In this work, the input...
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Springer Science and Business Media Deutschland GmbH
2022
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my.utm.1006702023-04-30T08:31:26Z http://eprints.utm.my/id/eprint/100670/ Machine vison-based system for vehicle classification and counting using YOLO Refaie, Elbaraa Mohd. Faudzi, Ahmad Athif TK Electrical engineering. Electronics Nuclear engineering This paper proposes a machine vision-based system that is used for vehicle classification and counting. Vehicle classifier and counter are a very crucial in road design to determine the road load capacity. It is also important to monitor, manage, and analyze the traffic flow. In this work, the input video is obtained from a static video camera and from drone-captured video to count the cars, determine its direction and to classify the vehicle types on the road. The data will be fed to the vision-based system that will pass the frames captured by the camera through a YOLO neural network to classify the vehicles. Then, the cars contours are used to determine the car path that determines in which direction the vehicle is moving before it is being counted. The system will display the total number of vehicles moved in each direction and while identifying each vehicle’s type. This work is possible to be used for other application such as surveillance tracking and monitoring. Springer Science and Business Media Deutschland GmbH 2022 Book Section PeerReviewed Refaie, Elbaraa and Mohd. Faudzi, Ahmad Athif (2022) Machine vison-based system for vehicle classification and counting using YOLO. In: Computational Intelligence in Machine Learning Select Proceedings of ICCIML 2021. Lecture Notes in Electrical Engineering, 834 (NA). Springer Science and Business Media Deutschland GmbH, Singapore, pp. 391-398. ISBN 978-981168483-8 http://dx.doi.org/10.1007/978-981-16-8484-5_37 DOI:10.1007/978-981-16-8484-5_37 |
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TK Electrical engineering. Electronics Nuclear engineering Refaie, Elbaraa Mohd. Faudzi, Ahmad Athif Machine vison-based system for vehicle classification and counting using YOLO |
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This paper proposes a machine vision-based system that is used for vehicle classification and counting. Vehicle classifier and counter are a very crucial in road design to determine the road load capacity. It is also important to monitor, manage, and analyze the traffic flow. In this work, the input video is obtained from a static video camera and from drone-captured video to count the cars, determine its direction and to classify the vehicle types on the road. The data will be fed to the vision-based system that will pass the frames captured by the camera through a YOLO neural network to classify the vehicles. Then, the cars contours are used to determine the car path that determines in which direction the vehicle is moving before it is being counted. The system will display the total number of vehicles moved in each direction and while identifying each vehicle’s type. This work is possible to be used for other application such as surveillance tracking and monitoring. |
| format |
Book Section |
| author |
Refaie, Elbaraa Mohd. Faudzi, Ahmad Athif |
| author_facet |
Refaie, Elbaraa Mohd. Faudzi, Ahmad Athif |
| author_sort |
Refaie, Elbaraa |
| title |
Machine vison-based system for vehicle classification and counting using YOLO |
| title_short |
Machine vison-based system for vehicle classification and counting using YOLO |
| title_full |
Machine vison-based system for vehicle classification and counting using YOLO |
| title_fullStr |
Machine vison-based system for vehicle classification and counting using YOLO |
| title_full_unstemmed |
Machine vison-based system for vehicle classification and counting using YOLO |
| title_sort |
machine vison-based system for vehicle classification and counting using yolo |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2022 |
| url |
http://eprints.utm.my/id/eprint/100670/ http://dx.doi.org/10.1007/978-981-16-8484-5_37 |
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1765296686235648000 |
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13.211869 |