Smart Intersection - daily traffic flow

WINLAB Summer Internship 2021

Group Members: Sandeep Alankar, Anthony Siu

Project Website

​https://bzz3ru.wixsite.com/smartintersection

Gitlab Repositories

DeepStream and YOLOv3 Application: ​https://gitlab.orbit-lab.org/si2020-smartintersection/smart-intersection-ds-yolov3-app

OpenCV- Add Bounding Boxes to Video: ​https://gitlab.orbit-lab.org/si2020-smartintersection/add-bounding-boxes

Project Objective

The goal of this project is to create a method for estimating the statistics for vehicle count/traffic flow into one intersection in New York City. As an example, record videos of the northbound traffic on Amsterdam Avenue, as vehicles are entering the 120th St./Amsterdam Av. intersection. Using YOLOv3 deep learning model, detect and count vehicles as they approach/enter the intersection from south, making sure that there is no double-counting. Use 180 second long video fragments (approximately two traffic light cycles), and repeat up to half a dozen times a day, for a number of workweek/weekend days during the same times of each day. Compare the vehicle count (traffic flow) as a function of the time of the day. Utilize NVIDIA DeepStream deployed on COSMOS GPU compute servers to run the model. The method should be generalizable/expandable to any direction of vehicle movement, when appropriate camera views are available.