A Haar Cascade is basically a classifier which is used to detect the object for which it has been trained for, from the source.
The Haar Cascade is trained by superimposing the positive image over a set of negative images. The training is generally done on a server and on various stages. Better results are obtained by using high quality images and increasing the amount of stages for which the classifier is trained.
Haar Cascade classifiers are an effective way for object detection. This method was proposed by Paul Viola and Michael Jones in their paper Rapid Object Detection using a Boosted Cascade of Simple Features .Haar Cascade is a machine learning-based approach where a lot of positive and negative images are used to train the classifier.
Positive images – These images contain the images which we want our classifier to identify. Negative Images – Images of everything else, which do not contain the object we want to detect. Requirements
Make sure you have python, Matplotlib and OpenCV installed on your pc (all the latest versions). The haar cascade files can be downloaded from the OpenCV Github repository.