Transferability of a Convolutional Neural Network (CNN) to Measure Traffic Density
Whereas detecting individual vehicles in a video image using a convolutional neural network (CNN) prevails for traffic surveillance, CNNs also have been successfully adapted to counting vehicles via a regression method, which conveys the advantages of simplifying the model structure, and inference t...
Main Authors: | Jiyong Chung, Gyeongjun Kim, Keemin Sohn |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-05-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/10/10/1189 |
Similar Items
-
Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point Detection Algorithm
by: Seungyun Jeong, et al.
Published: (2021-01-01) -
Non-Anchor-Based Vehicle Detection for Traffic Surveillance Using Bounding Ellipses
by: Byeonghyeop Yu, et al.
Published: (2021-01-01) -
Image-Based Learning to Measure the Space Mean Speed on a Stretch of Road without the Need to Tag Images with Labels
by: Jincheol Lee, et al.
Published: (2019-03-01) -
Turning traffic surveillance cameras into intelligent sensors for traffic density estimation
by: Zijian Hu, et al.
Published: (2023-06-01) -
A Lightweight Convolutional Neural Network (CNN) Architecture for Traffic Sign Recognition in Urban Road Networks
by: Muneeb A. Khan, et al.
Published: (2023-04-01)