SMART INTERSECTION
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2020 Summer Internship Program
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Rutgers University
Wireless Information Network Laboratory
OBJECTIVE
The goal of this project is to create a method for estimating vehicle count/traffic flow statistics for a given New York City intersection. YOLOv3 object detection running as an NVIDIA DeepStream application is used to count vehicles as they enter and exit intersections from various directions. Traffic flow is analyzed for comparison with flows from different days and time periods. Inference output is published to an AMQP message broker and can be recombined with the original video stream on separate machines via OpenCV.
WEEKLY PROGRESS
PUBLIC PRESENTATIONS
Public Presentations are presentations compiled and presented to general audiences who may not be familiar with our technologies and their technical details.
Check out our project poster and watch a video recording of our presentation
on the WINLAB 2020 Summer Internship Open House page!
INTERN CLASS
PRESENTATIONS
Intern Class Presentations are presentations compiled and presented to the entire intern class to confer knowledge gained about tools and software used by multiple internship projects.
TEAM PRESENTATIONS
Team Presentations are presentations compiled and presented to advisors during internal team meetings to deliver project updates and share new discoveries.
MEET THE TEAM
Team Google Drive (Internal)
WINLAB GitLab Group (Internal)
ACKNOWLEDGEMENT
We would like to thank the following individuals for advising and guiding us over the course of our WINLAB internship.
Ivan Seskar, Associate Director at WINLAB
Zoran Kostic, Associate Professor at Columbia University
Jennifer Shane, Laboratory Engineer at WINLAB
