Rezaei, M orcid.org/0000-0003-3892-421X, Azarmi, M orcid.org/0000-0003-0737-9204 and Mohammad Pour Mir, F (2023) 3D-Net: Monocular 3D object recognition for traffic monitoring. Expert Systems with Applications, 227. 120253. ISSN 0957-4174
Abstract
Machine Learning has played a major role in various applications including Autonomous Vehicles and Intelligent Transportation Systems. Utilizing a deep convolutional neural network, the article introduces a zero-calibration 3D Object recognition and tracking system for traffic monitoring. The model can accurately work on urban traffic cameras, regardless of their technical specification (i.e. resolution, lens, the field of view) and positioning (location, height, angle). For the first time, we introduce a novel satellite-ground inverse perspective mapping technique, which requires no camera calibrations and only needs the GPS position of the camera. This leads to an accurate environmental modeling solution that is capable of estimating road users’ 3D bonding boxes, speed, and trajectory using a monocular camera. We have also contributed to a hierarchical activity/traffic modeling solution using short- and long-term Spatio-temporal video analysis to understand the heatmap of the traffic flow, bottlenecks, and high-risk zones. The experiments are conducted on four datasets: MIO-TCD, UA-DETRAC, GRAM-RTM, and Leeds-Dataset including various use cases and traffic scenarios.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. |
Keywords: | Intelligent Transportation Systems (ITS), Traffic monitoring, Autonomous vehicles, Computer vision, Object recognition, Machine learning, Camera auto-calibration, Future mobility |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Funding Information: | Funder Grant number EU - European Union 101006664 |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Apr 2023 14:08 |
Last Modified: | 25 Jun 2023 23:19 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.eswa.2023.120253 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:198466 |