COVID-19 has affected everyone’s lives. Most companies and services have found new ways to approach their work while at the same time preventing further spread of the virus. Mode of travel has also significantly changed. There are high risks of protocol breaches at airports and railway stations. The proposed work is to ensure the safety of people, helping them have a safer journey. The idea is to ensure that people are maintaining social distancing and wearing a mask. The main aim of the proposed work is to reduce the contact between staff and the passengers. To validate the Aadhaar Card (SSN) and other IDs, the system uses face detection algorithms which will detect the faces and only allow those who have their tickets booked for that particular day.

What it does

  1. A Deep Learning Model to ensure social distancing and mask recognition through live camera feed at airport.
  2. A Face-Detection Algorithm to automate ticket checking and giving boarding pass. ## How we built it We used Tensorflow and Keras to train the deep learning model for Face Detection and Social Distancing. Image processing algorithm for face-detection of passengers. Used Flask for integrating the model and the Web-UI. ## Challenges we ran into
  3. Integrating the deep learning model to a web-interface.
  4. Obtaining sufficient accuracy while training the models
  5. To not have any latency in prediction and maintaining a high fps ## Accomplishments that we're proud of To develop a hassle free system which is ready to be deployed in a real world airport and can provide an instant effect. ## What we learned
  6. How to apply deep learning for a real world problem
  7. Integrating a Machine Learning Model to a webpage. ## What's next for CatchFlightsNotCOVID To add additional features so as the system identifies the people who aren't following the procedures and issue and warning via audio rather than the camera monitor instructing by watching the camera feed.

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