This repository is for the project of the "Sistemi digitali M" (Digital Systems M) master course exam at Unibo.
In this project, we took advantage of TensorFlow 2 object detection API to keep track of the number of people entering an environment with and without a protective facial mask against COVID-19 virus.
First, a neural network (an SSD MobileNet V2 FPNLite 320x320) has been trained to locate and distinguish between 2 different categories of objects:
- human faces (without a mask)
- human faces wearing a mask
To do that the following datasets have been used:
- https://www.kaggle.com/andrewmvd/face-mask-detection
- https://www.kaggle.com/wobotintelligence/face-mask-detection-dataset
Then, a simple - yet effective - tracking algorithm has been implemented in order to follow the movements of the objects over successive frames. This way we can determine whether a person enters the room, incrementing the counter corresponding to the object category if a virtual line is crossed.
Project presentation slides: PresentazioneSistDig.pdf
Full report: relazioneEsameSistemiDigitali.pdf
