SMART COVID SPREAD SYSTEM

To create safe environment that contributes to public safety, we propose an efficient computer vision based approach focused on the real-time automated monitoring of people to detect both safe social distancing and face masks in public places or other places by implementing the model to monitor acti

Project Title

SMART COVID SPREAD SYSTEM

Project Area of Specialization

Internet of Things

Project Summary

To create safe environment that contributes to public safety, we propose an efficient computer vision based approach focused on the real-time automated monitoring of people to detect both safe social distancing and face masks in public places or other places by implementing the model to monitor activity through camera. We design a binary face classifier which can detect any face present in the frame irrespective of its alignment. We present a method to generate accurate face segmentation masks from any arbitrary size input image. After input image processed, output image make bounding box around the faces. There will be thermal sensor that detects the human’s temperature and boom barrier in front of walk through gate. After check both conditions (face mask through camera and human temperature from thermal senor) true then it will sanitize the spray and open boom barrier and person passed from this barrier. Only if also one condition false then the boom barrier will not open and person will not passed from this barrier. And alert sound will be activated that for which reason the person will not passed, either person not weared the mask or person’s temperature high. And store persons data like temperature, recording, images and quantity etc in Cloud.

Project Objectives

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus.Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment.

The best way to prevent and slow down transmission is to be well informed about the COVID-19 virus, the disease it causes and how it spreads. Protect yourself and others from infection by cover your mouth and nose, wash your hands.

In the most common symptom is fever that is human’s temperature will be high. Then the problem is how to analyze person or detect person which have this symptom?

The solution of this problem is to put thermal sensor which will detect the person’s temperature then analyze symptoms conditions are true or not.

Face Mask Detection is such a necessity in today to safe and secure our environment from COVID-19 disease. Otherwise it will spread again all over the world.

Project Implementation Method

Phase 1

1. Strategic Analysis and Planning
2. Information Architecture
3. Wireframes
4. Technical System Design

Phase 2

1. Graphic and User Interface Design
2. Content Creation
3. Implementation
4. Testing

Phase 3

1. Testing
2. Maintenance

Benefits of the Project

Our architecture can be made compatible with TensorFlow RunTime (TFRT), which will increase the inference performance on edge devices and make our models efficient on multithreading CPUs.

Stage 1 and Stage 2 models can be easily replaced with improved models in the future, that would give better accuracy and lower latency.

Coughing and Sneezing Detection: Chronic coughing and sneezing is one of the key symptoms of COVID-19 infection as per WHO guidelines and also one of the major route of disease spread to non-infected public. Deep learning based approach can be proved handy here to detect & limit the disease spread by enhancing our proposed solution with body gesture analysis to understand if an individual is coughing and sneezing in public places while breaching facial mask and social distancing guidelines and based on outcome enforcement agencies can be alerted.

Temperature Screening: Elevated body temperature is another key symptom of COVID-19 infection, at present scenario thermal screening is done using handheld contactless IR thermometers where health worker need to come in close proximity with the person need to be screened, the proposed use-case can be equipped with thermal cameras based screening to analyze body temperature of the peoples in public places that can add another helping hand to enforcement agencies to tackle the pandemic effectively.

Technical Details of Final Deliverable

Dlib - The Dlib Deep Learning face detector offers significantly better performance than its precursor, the Dlib HOG based face detector.

MTCNN - It uses a cascade architecture with three stages of CNN for detecting and localizing faces and facial keypoints.

RetinaFace - It is a single-stage design with pixel-wise localization that uses a multi-task learning strategy to simultaneously predict face box, face score, and facial keypoints.

DIP – Digital Image Processing in which take real time servillance photage and process it.

IOT – Internet Of Things which take camera, thermal sensors, spar machine, boom barrier.

Data Science that is keep record of persons data like its temperature, recording, images and quantity etc.

Cloud Computing – This will cloud based in which all the data will store in the cloud.

Proper Authentication.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Security

Core Technology

Internet of Things (IoT)

Other Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com