= Smart Intersection - daily traffic flow  =

== Project Objective ==

The goal of this project is to create a method for estimating the statististics 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 Yolo V3 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.

== Reading Material ==


== Week 1 Activities ==

* Learn about YoloV3 deep learning models for object detection
* Read about NVIDIA deepstream
* Explore the image (set of computing tools) available on COSMOS, which uses deepstream and can deploy YoloV3 
* Record and save 6 videos during one day (to be repeated when the method is debugged and fully functional)
* Brainstorm about vehicle counting/traffic flow estimation methodology