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Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images
Nilani Algiriyage
author
Raj Prasanna
author
Emma E H Doyle
author
Kristin Stock
author
David Johnston
author
2020
Virginia Tech
Blacksburg, VA (USA)
English
Emergency Traffic Management (ETM) is one of the main problems in smart urban cities. This paper focuses on selecting an appropriate object detection model for identifying and counting vehicles from closed-circuit television (CCTV) images and then estimating traffic flow as the first step in a broader project. Therefore, a case is selected at one of the busiest roads in Christchurch, New Zealand. Two experiments were conducted in this research; 1) to evaluate the accuracy and speed of three famous object detection models namely faster R-CNN, mask R-CNN and YOLOv3 for the data set, 2) to estimate the traffic flow by counting the number of vehicles in each of the four classes such as car, bus, truck and motorcycle. A simple Region of Interest (ROI) heuristic algorithm is used to classify vehicle movement direction such as \quotes{left-lane} and \quotes{right-lane}. This paper presents the early results and discusses the next steps.
CCTV Big Data
YOLOv3
Traffic Flow Estimation.
r.nilani@massey.ac.nz
exported from refbase (http://idl.iscram.org/show.php?record=2211), last updated on Mon, 29 Jun 2020 07:26:57 +0200
text
http://idl.iscram.org/files/nilanialgiriyage/2020/2211_NilaniAlgiriyage_etal2020.pdf
NilaniAlgiriyage_etal2020
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management
Iscram 2020
Amanda Hughes
editor
Fiona McNeill
editor
Christopher W. Zobel
editor
17th International Conference on Information Systems for Crisis Response and Management
2020
Virginia Tech
Blacksburg, VA (USA)
conference publication
100
109
2411-3396
978-1-949373-27-10
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