Traffic-Net: 3d Traffic Monitoring Using a Single Camera

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Traffic-Net: 3d Traffic Monitoring Using a Single Camera 21 Pages Posted: 23 Jul 2022 See all articles by Mahdi RezaeiMahdi RezaeiUniversity of LeedsMohsen AzarmiDepartment of Computer Engineering, Qazvin Azad University, Qazvin, IRFarzam Mohammad Pour MirFaculty of Computer Engineering, Tehran Azad University, Science \& Research Branch, IR Date Written: July 3, 2022 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 streams 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. 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 Suggested Citation: Suggested Citation Rezaei, Mahdi and Azarmi, Mohsen and Mohammad Pour Mir, Farzam, Traffic-Net: 3d Traffic Monitoring Using a Single Camera (July 3, 2022). Available at SSRN: https://ssrn.com/abstract=4152596 Mahdi Rezaei (Contact Author) University of Leeds ( email ) Leeds, LS2 9JTUnited Kingdom01133435342 (Phone) HOME PAGE: http://environment.leeds.ac.uk/transport/staff/9408/dr-mahdi-rezaei Mohsen Azarmi Department of Computer Engineering, Qazvin Azad University, Qazvin, IR ( email ) Farzam Mohammad Pour Mir Faculty of Computer Engineering, Tehran Azad University, Science \& Research Branch, IR ( email ) Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 3 Abstract Views 14 PlumX Metrics Related eJournals Machine Learning eJournal Follow Machine Learning eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 387 PAPERS 4,660 Robotics eJournal Follow Robotics eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 189 PAPERS 957 Transport, Energy & Environment eJournal Follow Transport, Energy & Environment eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 70 PAPERS 1,341 Transportation Planning & Policy eJournal Follow Transportation Planning & Policy eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 63 PAPERS 3,445 Other Information Systems & eBusiness eJournal Follow Other Information Systems & eBusiness eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 1 PAPERS 6,449 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo4 in 0.176 seconds