Social distancing, also called “physical distancing,” means keeping a safe space between yourself and other people who are not from your household.

To practice social or physical distancing, stay at least 6 feet (about 2 arm lengths) from other people who are not from your household in both indoor and outdoor spaces.

Objective is to build a Deep Learning model which can identify if the person is wearing a mask or not, also detecting if people vilating social distancing norms.

Using haar cascade to detect faces Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, "Rapid Object Detection using a Boosted Cascade of Simple Features" in 2001. It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. We'll be using a Haar Cascade Model trained to detect faces in order to obtain the bounding box coordinates of faces in an image.

Detecting social distancing violations This can be done by iterating over the coordinates of faces and calculating the distance for each possible pair, if the distance for a particular pair is less than MIN_DISTANCE then the bounding boxes for those faces are colored red. MIN_DISTANCE must be manually initialized in such a way that it corresponds to the minimum allowable distance in real life (ex. 6ft in India).