Alright! This is where we start having some fun! The concept behind the Haar Cascade and how it is used in the real world is nothing short of amazing. So what is it?
Haar Cascade is a machine learning object detection algorithm used to identify objects in an image or video and based on the concept of features proposed by Paul Viola and Michael Jones in their paper "Rapid Object Detection using a Boosted Cascade of Simple Features" in 2001.
Haar 级联分类器是一种机器学习算法，最初由 Paul Viola 和 Michael Jones 在 2001 年的论文《Rapid Object Detection using a Boosted Cascade of Simple Features》中提出。该算法能够通过特征来识别图像或视频中的物体。
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.
[What’s the Difference Between Haar-Feature Classifiers and Convolutional Neural Networks?](https://towardsdatascience.com/whats-the-difference-between-haar-feature-classifiers-and-convolutional-neural-networks-ce6828343aeb)
[Computer Vision — Detecting objects using Haar Cascade Classifier](https://towardsdatascience.com/computer-vision-detecting-objects-using-haar-cascade-classifier-4585472829a9)
[Deep Learning Haar Cascade Explained](http://www.willberger.org/cascade-haar-explained/)
