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

= Smart Intersection - daily traffic flow  =

== Project Website == 
https://bzz3ru.wixsite.com/smartintersection

== 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.

== Reading Material ==


== Week 1 Activities ==

* Get ORBIT/COSMOS account and familiarize oneself with the testbed procedures
* 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


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**Week 1 Weekly Meeting Presentation:** https://docs.google.com/presentation/d/1Sf9hzpo3WQsEPwbhKfic2xWCskH1EViD-3SNb_foouA/edit?usp=sharing

== Week 2 Activities ==

* Understand the concepts of object detection in 3D Point Cloud
* Gain an understanding of NVIDIA’s !DeepStream SDK
* Get comfortable deploying YOLOv3 on the COSMOS testbed
* Use existing datasets to play around with !DeepStream and YOLOv3

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**Week 2 Weekly Meeting Presentation:** https://docs.google.com/presentation/d/1Cl8MbsSU3ZAq5lpRuE0eVBSwnIX4jTUAci5uAgP7Vt8/edit?usp=sharing

**Week 2 Team Meeting Presentation:** https://docs.google.com/presentation/d/1O2yCze4fmVOAFGCi0u6WTZq8VTFhLc_J448skeygguw/edit?usp=sharing

== Week 3 Activities ==

* Investigate existing RGB-D (RGB + depth map) object detectors whose models we can immediately put to use for inference
* Look into existing 3D Point Cloud object detection implementations
* Learn how to run !DeepStream's YOLOv3 implementation
* Investigate !DeepStream Python bindings for use with YOLO

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**Week 3 Weekly Meeting Presentation:** https://docs.google.com/presentation/d/13vqiw0kkyT0_XPzPv3NiowIvxc22SapfxM_ZbAKn1Cc/edit?usp=sharing

**Week 3 Team Meeting Presentation:** https://docs.google.com/presentation/d/1jwq6h05mw1vHt6_C1Br4MM_0LQDZRlIEoSvEasJ5Rsg/edit?usp=sharing

== Week 4 Activities ==

* Investigate YOLOv4 and its use with TensorRT
* Look into getting output/data processing based on the outputs from !DeepStream
* Look into the !DeepStream tracker to build on top of
* Build a presentation slide set to inform the intern class about !DeepStream and YOLOv3

''**Introduction to !DeepStream and YOLOv3 Presentation Slides:**'' ''To be posted at a later date''

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**Week 4 Weekly Meeting Presentation:** ''To be posted at a later date''

**Week 4 Team Meeting Presentation:** ''To be posted at a later date''