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Real-Time Big Data Analytics for Traffic Monitoring and Management for Pedestrian and Cyclist Safety

Authors:
Mohammad Pourhomayoun
Haiyan Wang
Mohammad Vahedi
Mehran Mazari
Hunter Owens
Janna Smith
William Chernicoff

Keywords: Machine Learning; Deep Learning; Computer Vision; Object Detection

Abstract:
In this study, we design and develop an end-to-end system based on data analytics and deep learning methods to monitor, count, and manage traffic, particularly, pedestrians and bicyclists in real-time. The main objective of this research is to improve the safety of pedestrians and bicyclists, by applying self-sensed and intelligent systems to control and monitor the flow of pedestrians/bicyclists particularly at intersections. This paper proposes an effective end-to-end system for traffic vision, detection, and counting on real-time traffic videos. The developed system is evaluated on 12 hours of real video streams captured from actual traffic cameras in the city of Los Angeles. According to the results, the developed system can count the pedestrians with less than 2% error.

Pages: 25 to 28

Copyright: Copyright (c) IARIA, 2019

Publication date: March 24, 2019

Published in: conference

ISSN: 2519-8386

ISBN: 978-1-61208-700-9

Location: Valencia, Spain

Dates: from March 24, 2019 to March 28, 2019