Automatic Traffic Monitoring and Management for Pedestrian and Cyclist Safety Using Deep Learning and Artificial Intelligence

Automatic Traffic Monitoring and Management for Pedestrian and Cyclist Safety Using Deep Learning and Artificial Intelligence

Abstract: 

In this project, we have designed and developed an effective end-to-end system based on advanced Artificial Intelligence (AI), machine learning, and computer vision to automatically monitor, detect, track, and count pedestrians and bicyclists. The main objective of this project is to improve the safety of pedestrians and bicyclists, by applying self-sensed and AI-powered systems to monitor and control the flow of pedestrians/bicyclists. The developed system includes algorithms for detecting the pedestrians and bicyclists, as well as algorithms for tracking and counting the pedestrians. We evaluated the developed system on real videos captured by actual traffic cameras in the city of Los Angeles. Despite the low quality of some of the videos, the results demonstrated high accuracy and effectiveness of the developed system in automatically detecting and counting pedestrians and bicyclists.

Authors: 

Mohammad Pourhomayoun, PhD

Dr. Mohammad Pourhomayoun is an Assistant Professor of Computer Science at California State University Los Angeles (CSULA). He is the founder and director of Artificial Intelligence & Data Science Research Lab at CSULA. Dr. Pourhomayoun’s research interests focus on Data Science, Artificial Intelligence (AI), and Machine Learning for social good.

Published: 

September 2020

Keywords: 

Artificial intelligence
Machine learning
Pedestrian counts
Pedestrian safety
Traffic safety