toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
  Record Links
Author (up) Nilani Algiriyage; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; David Johnston; Minura Punchihewa; Santhoopa Jayawardhana pdf  openurl
  Title Towards Real-time Traffic Flow Estimation using YOLO and SORT from Surveillance Video Footage Type Conference Article
  Year 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021  
  Volume Issue Pages 40-48  
  Keywords Computer Vision, Traffic Flow, YOLOv4, CCTV Big Data  
  Abstract Traffic emergencies and resulting delays cause a significant impact on the economy and society. Traffic flow estimation is one of the early steps in urban planning and managing traffic infrastructure. Traditionally, traffic flow rates were commonly measured using underground inductive loops, pneumatic road tubes, and temporary manual counts. However, these approaches can not be used in large areas due to high costs, road surface degradation and implementation difficulties. Recent advancement of computer vision techniques in combination with freely available closed-circuit television (CCTV) datasets has provided opportunities for vehicle detection and classification. This study addresses the problem of estimating traffic flow using low-quality video data from a surveillance camera. Therefore, we have trained the novel YOLOv4 algorithm for five object classes (car, truck, van, bike, and bus). Also, we introduce an algorithm to count the vehicles using the SORT tracker based on movement direction such as ``northbound'' and ``southbound'' to obtain the traffic flow rates. The experimental results, for a CCTV footage in Christchurch, New Zealand shows the effectiveness of the proposed approach. In future research, we expect to train on large and more diverse datasets that cover various weather and lighting conditions.  
  Address Massey University; Massey University; Massey University; Joint Centre for Disaster Research, Massey University; Joint Center of Disaster Research, Massey University Wellington; University of Kelaniya; Univerity of Kelaniya  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-61-5 ISBN Medium  
  Track AI and Intelligent Systems for Crises and Risks Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management  
  Notes Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2311  
Share this record to Facebook
Select All    Deselect All
 |   | 

Save Citations:
Export Records: