Multi-Camera Detection of Social Distancing Reference Implementation

Published: 05/04/2020

Edge Software Hub   /   Multi-Camera Detection of Social Distancing  /  Documentation

Overview

Social distancing and face masks are one of the most effective nonpharmaceutical ways to prevent the spread of disease. This reference implementation gives a solution to prevent the spread of disease by using computer vision inference in the Intel® Distribution of OpenVINO™ toolkit to measure distance between people and store data to InfluxDB. This data can be visualized on a Grafana* dashboard.

The reference implementation and the software listed below are installed when selected as part of the Edge Insights for Vision package. If you have not installed that package yet, select Configure & Download to download the reference implementation and then follow the installation instructions for Edge Insights for Vision.  

Configure & Download

Time to Complete

Programming
Language

Available Software

50 - 70 minutes

Python* 3.6


Intel® Distribution of OpenVINO™
toolkit 2021 Release

Recommended Hardware
The below hardware is recommended for use with this reference implementation. See the Recommended Hardware page for other suggestions. 

 

Target System Requirements

  • Ubuntu* 18.04.3 LTS/CentOS 7.x
  • 6th to 11th Generation Intel® Core™ processors with Intel® Iris® Plus Graphics or Intel® HD Graphics
  • USB webcam

 

How It Works

A multi-camera surveillance solution demonstrates an end-to-end analytics pipeline to detect people and calculates social distance between people from multiple input feeds. Frames are transformed, scaled and normalized into BGR images which can be fed to the inference engine in the Intel® Distribution of OpenVINO™ toolkit. The steps below are performed for the inference.

  • Apply Intel's person detection model, i.e., person-detection-retail-0013 to detect people from all the video streams.

  • Compute Euclidean distance between all the people from the above step.

  • Based on above measurements, check whether any people are violating N pixels apart.

  • Store total violations count of social distancing data in InfluxDB.

  • Visualize the stored data of InfluxDB on Grafana dashboard.

Figure 1: Architecture Diagram

 

Get Started

Step 1: Install the Reference Implementation 

NOTE: If you have installed Edge Insights for Vision, the Reference Implementation was installed with the package and it is already available in the target system at /MultiCamera_Detection_of_Social_Distancing/mcss-covid19/ where is the Edge Insights version downloaded. (It can be found in the readme file.)

 

Step 2: Download the Input Video

The application works better with input feed in which cameras are placed at eye level angle.

Please download sample video at 1280x720 resolution and place it in the resources directory. (Data set subject to this license. The terms and conditions of the dataset license apply. Intel® does not grant any rights to the data files.)

To use any other video, specify the path in the run.sh file inside the application directory. For example, if two of the test video files are in the resources folder, then make changes to the following variables INPUT1 and MIN_SOCIAL_DIST1 for the test video.

INPUT1="${PWD}/../resources/<name_of_video_file>.mp4"
MIN_SOCIAL_DIST1=<appropriate_minimun_social_distance_for_input1>

(Optional) Test with USB Camera

To test with a USB camera, specify the camera index in the run.sh file.

On Ubuntu, to list all available video devices, run the following command:

ls /dev/video*

For example, if the output of the command is /dev/video0, then make changes to the following variables such as INPUT1 and MIN_SOCIAL_DIST1 in the run.sh file inside the application folder.

INPUT1=/dev/video0
MIN_DIST1=<appropriate_minimun_social_distance_for_input1>

 

Step 3: Initialize Environment Variables

Run the following command to initialize OpenVINO environmental variables:

source /opt/intel/openvino_2021/bin/setupvars.sh

 

 

Run the Application

 

Run the Application on CPU

  1. Change to the application directory: 
    cd application
  2. Inside the run.sh file, change the following parameters (if required): 
    PERSON_DETECTOR=”${PWD}/../intel/person-detection-retail-0013/FP16/person-detection-retail-0013.xml”
    DEVICE1="CPU" 
  3. Change the permissions for the run.sh file and run the script: 
    chmod +x run.sh
    ./run.sh 
    

 

NOTE: Application parameters can be changed as per the requirements in the run.sh file. 

 

Run the Application on GPU

  1. Change to the application directory: 
    cd application
  2. Inside the run.sh file, change the following parameters (if required): 
    PERSON_DETECTOR=”${PWD}/../intel/person-detection-retail-0013/FP16/person-detection-retail-0013.xml”
    DEVICE1="GPU" 
  3. Change the permissions for the run.sh file and run the script: 
    chmod +x run.sh
    ./run.sh 
    

 

Run the Application on HDDL

  1. Change to the application directory: 
    cd application
  2. Inside the run.sh file, change the following parameters (if required): 
    PERSON_DETECTOR=”${PWD}/../intel/person-detection-retail-0013/FP16/person-detection-retail-0013.xml”
    DEVICE1="HDDL" 
  3. Change the permissions for the run.sh file and run the script: 
    chmod +x run.sh
    ./run.sh 
    

 

Figure 2: Application Running on Four Channels

 

 

Data Visualization on Grafana

1. Navigate to localhost:3000 on your browser.

NOTE: If browser shows Unable to connect then make sure Grafana service status is active using the command sudo service grafana-server status
If service is not active, then start the service by running the command sudo service grafana-server start in the terminal.

2. Login with user as admin and password as admin.

3. Go to Configuration and select Data Sources.

4. Click + Add data source, select InfluxDB, and provide the following details:

     Name: Mcss Covid

     URL: http://localhost:8086

     Auth: Choose skip TLS Auth

     InfluxDB details: 

          Database: McssCovid

          HTTPMethod: GET

5. Click Save and Test.

Figure 3: Data Source Creation

 

6. Click + icon on the left side of the window, then select Import.
7. Choose Upload.json File and import "mcss-covid19/resources/multi_cam.json" file.
8. Click on Import.
 

Figure 4: Import Dashboard

 

9. Click on Multi Camera Covid-19 Solution dashboard to view real time violation data.

Figure 5: Final Dashboard

 

 

Summary and Next Steps

This application successfully leverages Intel® Distribution of OpenVINO™ toolkit plugins for detecting and measuring distance between the people and storing data to InfluxDB. It can be extended further to provide support for feed from network stream (RTSP camera) and the algorithm can be optimized for better performance.

Create a Microsoft Azure* IoT Central Dashboard 

As a next step, you can create an Azure* IoT Central dashboard for this reference implementation, run standalone Python code to fetch telemetry data from Influx DB, and send data to the Azure IoT Central dashboard for visualizing telemetry data. See Connect Edge Devices to Azure IoT* for instructions.

Learn More

To continue learning, see the following guides and software resources:

Troubleshooting

If you're unable to resolve your issues, contact the Support Forum.  

 

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.