Social-distancing is an important way to slow down the spread of infectious diseases. People are asked to limit their interactions with each other, reducing the chances of the disease being spread with physical or close contact.
In the past decade, Machine learning and Artificial Intelligence has shown promising results in several daily life problems. Various daily life tasks have been automated with the help of ML and AI . So that inspired me to contribute to the world a solution against this deadly virus and the only possible way is to maintain social distance (even after wearing a mask) . It is difficult to verify and ensure everyone are maintaining social distancing or not , So it was my inspiration to make it possible for everyone.
It identifies the people who not well distanced
I built it using YOLO , YOLO divides up the image into a grid of N by N cells. Each of these cells is responsible for predicting a finite number of bounding boxes. A bounding box describes the rectangle that encloses an object. YOLO also outputs a confidence score that tells us how certain it is that the predicted bounding box actually encloses some object.
YOLO apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections.
YOLO uses a totally different approach. It applies a single neural network to the full image. This network divides the image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities.
I ran into many challenges and it took some time to correct it.
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