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

Pourhomayoun, Mohammad

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

2020-09-01
By Pourhomayoun, Mohammad

File Language:

English

Details

Creators:

Pourhomayoun, Mohammad
Corporate Creators:

Mineta Consortium for Transportation Mobility
Corporate Contributors:

United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; San Jose State University. College of Business ; United States. Department of Transportation. University Transportation Centers (UTC) Program
Subject/TRT Terms:
Publication/ Report Number:

20-29
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

Mineta Transportation Institute
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.
Format:

PDF
Funding:

65A0660
Download URL:

https://rosap.ntl.bts.gov/view/dot/51786/dot_51786_DS1.pdf
File Type:

[PDF - 2.27 MB]
Collection(s):

University Transportation Centers ; US Transportation Collection
Main Document Checksum:

urn:sha-512:5c881d733327467781b55cef33327f134f14862ec85e65b97c8e318810e15b038dbac33b109f1d3b024c4f61aebf5a448f4eac7ad5de97107c83b4ebd9d371bb

File Language:

English

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