ASMA - Autonomous Social-Distancing Monitoring Agent

Introduction

ASMA is an application to quantify social distancing measures using edge computer vision systems. Since all computation runs on the device, it requires minimal setup and minimizes privacy and security concerns. It can be used in retail, workplaces, schools, construction sites, healthcare facilities, factories, etc.

This application measures social distancing rates and gives proper notifications each time someone ignores social distancing rules. By generating and analyzing data, this solution outputs statistics about high-traffic areas that are at high risk of exposure to COVID-19 or any other contagious virus. The project is under substantial active development.

Getting Started

You can read the Smart Social Distancing tutorial on our website. The following instructions will help you get started.

Prerequisites

Hardware
A drone:

• Custom Made 250mm diagonal frame with PixHawk and Raspberry Pi.

A host edge device:

• NVIDIA Jetson Nano
• Raspberri Pi 4
• X86 node (also accelerated with Openvino)

Software

• You should have Docker on your device.

Make sure you have the prerequisites and then clone this repository to your local system by running this command:

```bash
git clone https://github.com/nadeembinshajahan/asma
cd asma

Drone Architecture

The Aerial Agent consists of a 250mm custom built drone using pixhawk 2.4.8 flight controller flashed with the ArduPilot autopilot. The agent is equipped with a Ublox Neo 7M GPS for self navigation.

For autonomous operation, an onboard single board computer is interfaced via the UART port of the flight controller. A Raspberry Pi 3B+, running raspbian stretch, is used as the onboard computer. For remote surveillance and operation, the agent is equipped with a JioFi 4G dongle. A Raspberry PiCam captures the video feed for monitoring, which is then streamed to a forwarded port, to enable remote monitoring. The onboard SBC runs Mavros, which is a ROS wrapper for the MavLink protocol. This enables the agent to be controlled remotely using the SBC.

Usage

Make sure you have Docker installed on your device by following these instructions.

The smart social distancing app consists of two components which must be run separately. There is the frontend and the processor. In the following sections we will cover how to build and run each of them depending on which device you are using.

Download Required Files

# Download a sample video file from multiview object tracking dataset
# The video is complied from this dataset: https://researchdatafinder.qut.edu.au/display/n27416
./download_sample_video.sh

Building the Docker image for frontend (This step is optional if you are not going to build any docker image)

The frontend consists of 2 Dockerfiles:

• frontend.Dockerfile: Builds the React app.
• web-gui.Dockerfile: Builds a FastAPI backend which serves the React app built in the previous Dockerfile.

If the frontend directory on your branch is not identical to the upstream master branch, you MUST build the frontend image with tag "neuralet/smart-social-distancing:latest-frontend" BEFORE BUILDING THE MAIN FRONTEND IMAGE. Otherwise, skip this step, as we have already built the frontend for master branch on Dockerhub.

• To build the frontend run:

docker build -f frontend.Dockerfile -t asma:frontend .

• To run the frontend, run:

docker build -f web-gui.Dockerfile -t asma:web-gui .
docker run -it -p HOST_PORT:8000 --rm asma:web-gui 

Important: There is a config-frontend.ini file which tells the frontend where to find the processor container. You must set the "Processor" section of the config file with the correct IP and port of the processor.

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NOTE

Building the frontend is resource intensive. If you are planning to host everything on an edge device, we suggest building the docker image on your PC/laptop first and then copy it to the edge device. However, you can always start the frontend container on a PC/laptop and the processor container on the edge device.

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Run using OpenVino

# download model first
./download_openvino_model.sh

# 1) Build Docker image (This step is optional, you can skip it if you want to pull the container from neuralet dockerhub)
docker build -f x86-openvino.Dockerfile -t asma:backend .

# 2) Run Docker container:
docker run -it -p HOST_PORT:8000 -v "$PWD/data":/repo/data asma:backend

NOTE: residual files under data/web_gui/static/ may cause you to see previous streams and plots stored there! This needs to be issued separately, you can mannually clean that path for now.