Description
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.
Publication Date
9-2020
Publication Type
Report
Topic
Active Transportation, Transportation Technology
Digital Object Identifier
10.31979/mti.2020.1808
MTI Project
1808
Mineta Transportation Institute URL
https://transweb.sjsu.edu/research/1808-Automatic-Traffic-Monitoring-Pedestrian-Cyclist-Safety
Keywords
Artificial intelligence, Machine learning, Pedestrian counts, Pedestrian safety, Traffic safety
Disciplines
Transportation
Recommended Citation
Mohammad Pourhomayoun. "Automatic Traffic Monitoring and Management for Pedestrian and Cyclist Safety Using Deep Learning and Artificial Intelligence" Mineta Transportation Institute Publications (2020). https://doi.org/10.31979/mti.2020.1808
Research Brief
