Rezaei, M orcid.org/0000-0003-3892-421X, Azarmi, M and Mir, FMP (2021) Traffic-Net: 3D Traffic Monitoring Using a Single Camera. [Preprint - arXiv]
Abstract
Computer Vision has played a major role in Intelligent Transportation Systems (ITS) and traffic surveillance. Along with the rapidly growing automated vehicles and crowded cities, the automated and advanced traffic management systems (ATMS) using video surveillance infrastructures have been evolved by the implementation of Deep Neural Networks. In this research, we provide a practical platform for real-time traffic monitoring, including 3D vehicle/pedestrian detection, speed detection, trajectory estimation, congestion detection, as well as monitoring the interaction of vehicles and pedestrians, all using a single CCTV traffic camera. We adapt a custom YOLOv5 deep neural network model for vehicle/pedestrian detection and an enhanced SORT tracking algorithm. For the first time, a hybrid satellite-ground based inverse perspective mapping (SG-IPM) method for camera auto-calibration is also developed which leads to an accurate 3D object detection and visualisation. We also develop a hierarchical traffic modelling solution based on short- and long-term temporal video data stream to understand the traffic flow, bottlenecks, and risky spots for vulnerable road users. Several experiments on real-world scenarios and comparisons with state-of-the-art are conducted using various traffic monitoring datasets, including MIO-TCD, UA-DETRAC and GRAM-RTM collected from highways, intersections, and urban areas under different lighting and weather conditions.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an open access preprint under the terms of the Creative Commons Attribution-Noncommercial-Sharealike 4.0 International Licence. |
Keywords: | 3D Object Detection; Traffic Flow Monitoring; Intelligent Transportation Systems; Deep Neural Networks; Vehicle Detection; Pedestrian Detection; Inverse Perspective Mapping Calibration; Digital Twins, Video Surveillance |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Dec 2024 15:25 |
Last Modified: | 13 Dec 2024 08:53 |
Published Version: | https://arxiv.org/abs/2109.09165 |
Identification Number: | 10.48550/arXiv.2109.09165 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179293 |
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Filename: Traffic-Net 3D Traffic Monitoring Using a Single Camera.pdf
Licence: CC-BY-NC-SA 4.0