Edge Insights for Vision

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ID 772621
Date 9/07/2022
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Document Table of Contents
Document Table of Contents x
Edge Insights for Vision How it Works Get Started Guide using Linux* Get Started Guide using Windows* Release Notes Documentation Archive
Get Started Guide using Linux* x
Linux* Requirements Install Edge Insights for Vision Introduction to the Edge Software Command Line Interface (CLI) Linux* Troubleshooting Linux* Tutorials
Linux* Tutorials x
Single and Multi-Object Detection with Hardware Acceleration on Linux* Multi-Camera Detection of Social Distancing on Linux*
Single and Multi-Object Detection with Hardware Acceleration on Linux* x
Single and Multi-Object Detection on CPU on Linux* Single and Multi-Object Detection on GPU on Linux* Single and Multi-Object Detection on Intel® Vision Accelerator on Linux*
Get Started Guide using Windows* x
Windows* Requirements Install Edge Insights for Vision Windows* Troubleshooting Windows* Tutorials
Windows* Tutorials x
Single and Multi-Object Detection with Hardware Acceleration on Windows* Multi-Camera Detection of Social Distancing on Windows* Overview How It Works Get Started Run the Application Data Visualization on Grafana Summary and Next Steps
Single and Multi-Object Detection with Hardware Acceleration on Windows* x
Single and Multi-Object Detection on CPU on Windows* Single and Multi-Object Detection on GPU on Windows* Single and Multi-Object Detection on <dfn class='term'>Intel</dfn> ® Vision Accelerator on Windows*
Edge Insights for Vision
How it Works
Get Started Guide using Linux*
Get Started Guide using Windows*
Release Notes
Documentation Archive

Multi-Camera Detection of Social Distancing on Windows*

Overview

Social distancing is one of the most effective non-pharmaceutical ways to prevent the spread of disease. This Windows* tutorial gives a solution to prevent the spread of disease by using the Inference Engine in the Intel® Distribution of OpenVINO™ toolkit and computer vision / Open CV to measure distance between people and store data to InfluxDB*. This data can be visualized on a Grafana* dashboard.

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.

Get Started

Step 1: Install the Component

The Multi-Camera Detection of Social Distancing component will be installed with the Edge Insights for Vision package package and will be available in the target system.

Go to the Multi-Camera Detection of Social Distancing component directory which will be available at the path where you started the installation of Edge Insights for Vision from edgesoftware.exe: 

C:\Program Files (x86)\Intel\Edge_Insights_for_Vision_2021.4_LTS\Edge_Insights_for_Vision_<version>\RI_Multi_Camera\mcss-covid19\

Where <version> is the Edge Insights for Vision version selected while downloading.

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 <path_of_edge_insights_vision>\Edge_Insights_for_Vision_<version>\RI_Multi_Camera\mcss-covid19\resources directory.

Where <version> is the Edge Insights for Vision version selected while downloading.

(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 INPUT1 in the run.py file inside the application directory.

The application also supports multi-video as input. The appropriate code with comments is available in the run.py file inside the application directory. 

NUM_CHANNELS=4
INPUT1=p+"\\..\\resources\\Pexels Videos 2670.mp4"
MIN_SOCIAL_DIST1==<appropriate_minimum_social_distance_for_input1>

Where: <appropriate_minimum_social_distance_for_input1> is measured in cm. 80 cm is recommended.

Step 3: Initialize Environment Variables

  1. Open a Command Prompt in administrator mode.

  2. Go to the bin directory located in the Intel® Distribution of OpenVINO™ toolkit installation path: 

    cd C:\Program Files (x86)\Intel\openvino_2021\bin

  3. Run setupvars.bat

Leave the command prompt window open for the next step.

Run the Application

Instructions in this tutorial are provided for two hardware configurations (CPU and GPU). Configure the application by modifying the DEVICE1 parameter.

  1. Go to the application directory of Multi-Camera Detection of Social Distancing component: 

    cd C:\Program Files (x86)\Intel\Edge_Insights_for_Vision_2021.4_LTS\Edge_Insights_for_Vision_<version>\RI_Multi_Camera\mcss-covid19\application\

    Where <version> is the Edge Insights for Vision version selected while downloading.

  2. Inside the run.py file, change the following parameters (if required): 

    PERSON_DETECTOR=p+"\\..\\intel\\person-detection-retail-0013\\FP16\\person-detection-retail-0013.xml"
"DEVICE1="<device>"

    where <device> can be either CPU or GPU.

  3. Run the script from the command prompt: 

    python run.py

The following sample output shows the application running on four channels.

NOTE:

You can change application parameters in the run.py file.

Video decode device (DECODE_DEVICE) works only with CPU (DECODE_DEVICE="CPU").

Data Visualization on Grafana

NOTE:

The application must be running in parallel to view the results in Grafana.

  1. Navigate to localhost:3000 on your browser.

    NOTE:
    If browser shows localhost refused to connect, then make sure Grafana service status is active.

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

    You can skip the Change Password page if it is displayed.

  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 Verify
InfluxDB details:
    Database: McssCovid
    HTTPMethod: GET
  5. Click Save and Test.
  6. Click + icon on the left side of the window, then select Import.
  7. Choose Upload.json File and import the mcss-covid19/resources/multi_cam.json file.
  8. Click on Import.

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

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.

As a next step, you can explore other use cases and reference implementations on the Edge Insights for Vision page.

Parent topic: Windows* Tutorials
Level Two Title