Deploy a People Counter App at the Edge

What it Does

The people counter application will demonstrate how to create a smart video IoT solution using Intel® hardware and software tools. The app will detect people in a designated area, providing the number of people in the frame, average duration of people in frame, and total count.

Aim

• Create an app that will help maintain social distancing at public places likes grocery stores and shopping malls, to curb the spread of Covid-19 virus
• The app will detect people in a designated area, providing the number of people in the frame, average duration of people in frame, and total count.
• The people counter application is a smart video IoT solution based on Intel® hardware and software tools.
• Utilizes deep learning, image processing, and OpenVINO-based inferencing at the edge.

How it Works

The counter will use the Inference Engine included in the Intel® Distribution of OpenVINO™ Toolkit. The model used should be able to identify people in a video frame. The app should count the number of people in the current frame, the duration that a person is in the frame (time elapsed between entering and exiting a frame) and the total count of people. It then sends the data to a local web server using the Paho MQTT Python package.

You will choose a model to use and convert it with the Model Optimizer.

Requirements

Hardware

• 6th to 10th generation Intel® Core™ processor with Iris® Pro graphics or Intel® HD Graphics.
• OR use of Intel® Neural Compute Stick 2 (NCS2)
• OR Udacity classroom workspace for the related course

Software

• Intel® Distribution of OpenVINO™ toolkit 2019 R3 release
• Node v6.17.1
• Npm v3.10.10
• CMake
• MQTT Mosca server

Setup

Git: People Counter App

Reference Repo