Traffic Flow Estimation Based on Deep Learning for Emergency Traffic Management using CCTV Images
presentationposted on 07.12.2020, 22:03 by Rangika Nilani
Traffic flow estimation is the first step in the management of road traffic infrastructure and is essential for the successful deployment of intelligent transportation systems. Closed-circuit television (CCTV) systems are now popular and are mounted in many public places to support real-time surveillance. The data generated by CCTV cameras can be used as the foundation for accurate traffic flow estimation.
The lightning talk is based on research carried out seeking to answer the questions; 1) What object detection algorithm is best suited to the CCTV image data set for vehicle detection? 2) Can traffic flow be estimated by counting the number of vehicles in CCTV images using an object detection algorithm?
We collect real-time CCTV imagery from traffic cameras through the New Zealand Transport Agency’s (NZTA) traffic cameras Application Programming Interface (API). In the first experiment, we compare the performance and accuracy of faster R-CNN, Mask R-CNN and YOLOv3 algorithms in vehicle detection task. Then, we select a case study at one of the busiest roads in Christchurch Central Business District (CBD) to estimate the traffic flow. The results can be used by city authorities to understand traffic flow patterns, make traffic predictions, understand anomalies, and make management decisions.
ABOUT THE AUTHOR(S)
Nilani is a PhD candidate at the Joint Centre for Disaster Research at Massey University, Wellington
Raj is a senior lecturer at the Joint Centre for Disaster Research at Massey University, Wellington
Kristin is a Senior Lecturer in Information Technology and Director of the Massey Geoinformatics Collaboratory at Massey University, Auckland
Emma is a senior lecturer at the Joint Centre for Disaster Research at Massey University, Wellington
David Johnston is the Professor of Disaster Management and Director of the Joint Centre for Disaster Research at Massey University, Wellington