Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images

PhD Research Project by Nilani Rangika

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 “left-lane” and “right-lane”.

More details of this work can be found through the following link:

Nilani Algiriyage, Raj Prasanna, Emma E H Doyle, Kristin Stock, & David Johnston. (2020). Traffic Flow Estimation based on Deep Learning for Emergency Traffic Management using CCTV Images. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 100–109). Blacksburg, VA (USA): Virginia Tech.

http://idl.iscram.org/files/nilanialgiriyage/2020/2211_NilaniAlgiriyage_etal2020.pdf