The problem CovidOut solves

As schools reopen, students must social distance to prevent themselves and teachers from getting infected. However, it will be difficult to monitor each student, especially when the teacher is busy teaching. So with new technology, we can solve this problem by detecting if students are less than 6ft apart and alert the teacher, and show the region in the classroom where students are not complying. We also included a way for the app to detect if students are wearing their masks correctly and the teacher will be notified if a student takes off their mask for more than 5 seconds. With these analytics, the teacher can successfully alter the classroom environment if needed and better enforce safety rules. This will significantly prevent students from getting and spreading COVID-19 as well as prevent teachers (many of whom are more susceptible) from getting COVID-19.

Challenges we ran into

It was our first time using an open source neural network so figuring out how to connect these components in such little time was difficult. We had to go through a lot of documentation to learn about this technology and then it took hours to implement in our application. It was also difficult to be able to figure out the average area among all the “red zones” using OpenCV, but we are pleasantly surprised that we got it to work with great accuracy.
In terms of mask detection, we had a lot of difficulty using Linux to utilize YOLO software, OpenCV, and Darknet. While we did get some preliminary code working, we do not have a functioning product for face mask detection. Our face mask prototype in the demo simply showcases the UI we want to achieve. We are still working on training and testing our machine.