Create a Store Traffic Monitoring Solution

Track and count people inside and outside of a facility using three different video streams.

Target Operating System Ubuntu* 16.04 LTS
Time to Complete 50 - 70 minutes

GitHub* (C++)  GitHub (Python*)

What You Will Learn

Learn how to use a trained neural network to detect objects within a designated area by displaying a green bounding box over them. Monitor the activity of people inside and outside of a facility as well as count product inventory.

Gain insight into the following solutions:

  • Computer vision applications for IoT
  • Inference to analyze datasets
  • Retail market IoT

Use the skills learned in this reference implementation to develop similar IoT solutions.

Learn to build and run an application with these capabilities:

Monitor activity of people inside and outside of a facility.
Recognize when inventory on a shelf is getting low.
Use a trained neural network to detect objects.

How It Works

The application uses the inference engine included in the Intel® Distribution of OpenVINO™ toolkit. A trained neural network detects objects within a designated area by displaying a green bounding box over the objects. This reference implementation identifies multiple intruding objects entering the frame and identifies their class, count, and time entered.