## The problem it solves :

During these tough times when humanity is struggling to survive, **it is essential to maintain social distancing and proper hygiene.** As a big crowd now is approaching the **vaccination centres**, it is obvious that there will be overcrowding.

This project implements virtual queues which will ensure social distancing and allow people to stand separate instead of crowding near the counter or the reception site which is a evolving necessity in covid settings!

**“ With Quelix, you can just scan the OR code, enter the virtual world of queues and wait for your turn to arrive. Timely notifications will keep the user updated about his position in the Queue.”**

## Key-Features

* **Just scan the OR code!**
* **Enter the virtual world of queues and wait for your turn to arrive.**
* **Timely notifications/sound alerts will keep the user updated about his position/Time Left in the Queue.**
* **Automated Check-in Authentication System for Following the Queue.**
* **Admin Can Pause the Queue.**
* **Admin now have the power to remove anyone from the queue**
* Reduces Crowding to Great Extent.
* Efficient Operation with Minimum Cost/No additional hardware Required
* Completely Contactless

## Challenges we ran into :

* Simultaneous Synchronisation of admin & queue members with instant Updates.
* Implementing Queue Data structure in MongoDB
* Building OTP API just from scratch using Flask.

```
while(quelix.on):
        if covid_cases.slope<0:
                 print(True)
>>> True

```

[Github repo](https://github.com/Dart9000/Quelix2.0)

[OTP-API-repo](https://github.com/Dart9000/OTP-flask-API)

[Deployment](https://quelix.herokuapp.com/)
## Inspiration

Every year hundreds of thousands of preventable deaths occur due to the lack of first aid knowledge in our societies. Many lives could be saved if the right people are in the right places at the right times. We aim towards connecting people by giving them the opportunity to help each other in times of medical need.

## What it does

It is a mobile application that is aimed towards connecting members of our society together in times of urgent medical need. Users can sign up as respondents which will allow them to be notified when people within a 300 meter radius are having a medical emergency. This can help users receive first aid prior to the arrival of an ambulance or healthcare professional greatly increasing their chances of survival. This application fills the gap between making the 911 call and having the ambulance arrive.

## How we built it

The app is Android native and relies heavily on the Google Cloud Platform. User registration and authentication is done through the use of Fireauth. Additionally, user data, locations, help requests and responses are all communicated through the Firebase Realtime Database. Lastly, the Firebase ML Kit was also used to provide text recognition for the app's registration page. Users could take a picture of their ID and their information can be retracted.

## Challenges we ran into

There were numerous challenges in terms of handling the flow of data through the Firebase Realtime Database and providing the correct data to authorized users.

## Accomplishments that we're proud of

We were able to build a functioning prototype! Additionally we were able to track and update user locations in a MapFragment and ended up doing/implementing things that we had never done before.
## Inspiration

After seeing how things unfolded due to crowd crushing during a music concert like Astroworld, and more recently in Itaewon, in South Korea, we were compelled to think of a solution in order to prevent such disasters from happening again. From our observations, there seemed to be a lack of infrastructure to properly perform crowd control. Our team has concluded that these catastrophic events could be prevented with the help of an app which could allow event staff or security guards quickly act when danger arises.

## What it does

Using multiple person pose detection, our app tracks people's locations and maps them to a heatmap on the frontend. Using the heatmap, staff can visualize the current state of the location they are looking at. Users can switch between views, which correspond to camera views and locations. The application also allows staff to see the location of their team members and communicate with them via messaging or sending emergency pings.

## How we built it

In the back end, we use tensorflow and the MoveNet Lightning model to perform multiple persone pose detection. Our Flask API processes GET requests to send an array of points corresponding to people's locations on the heatmap.

Our React front end sends GET requests to the Flask API to receive the coordinates and compute the heatmap. We also use Firestore to store the user's plans and favorites.

## Challenges we ran into

The main challenge we ran into was the limitations of MoveNet Lightning. It is a fast model, but it only detects up to 6 people, which does not allow us to detect enough people in the crowd. Using another model, such as OpenPose, happened to be quite challenging and time consuming to set up due to outdated code, and training our own model was also not an option.

## Accomplishments that we're proud of

We are proud of having created a large web app where most functionalities are implemented.

## What we learned

Some of us learned how to use React for the first time and how to deal with a machine learning model.

## What's next for CrowdSpace

We have many ideas to make our app more reliable. By using OpenPose or a trained model, we wish to achieve slightly more reliable results using computer vision. Also, we were already aware of the limitations of computer vision, which is why we think it would be more accurate to use techniques such as Wifi positinioning, bluetooth positioning or GPS positioning.