[[TOC(Other/Summer/2020/SmartIntersection/*, depth=1, heading=Smart Intersection)]]

= 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.