In this tutorial, you will learn about OpenCV Haar Cascades and how to apply them to real-time video streams. Join us to find out in this article.
In this tutorial, you will learn about OpenCV Haar Cascades and how to apply them to real-time video streams.
Haar cascades, first introduced by Viola and Jones in their seminal 2001 publication, Rapid Object Detection using a Boosted Cascade of Simple Features, are arguably OpenCV’s most popular object detection algorithm.
Sure, many algorithms are more accurate than Haar cascades (HOG + Linear SVM, SSDs, Faster R-CNN, YOLO, to name a few), but they are still relevant and useful today.
One of the primary benefits of Haar cascades is that they are just so fast — it’s hard to beat their speed.
The downside to Haar cascades is that they tend to be prone to false-positive detections, require parameter tuning when being applied for inference/detection, and just, in general, are not as accurate as the more “modern” algorithms we have today.
That said, Haar cascades are:
In the remainder of this tutorial, you’ll learn about Haar cascades, including how to use them with OpenCV.
In this article, you will learn how haar cascade classifiers really work through python visualization functions. Haar Cascade Classifiers in OpenCV Explained Visually. Normally, every algorithm has a set of instructions, something without which this algorithm couldn’t exist on a basic level, and its remainder is as of now based on its instructions. In the Viola-Jones algorithm, these instructions are made up of Haar signs, which are a set of rectangular kernels.
In this video we demonstrate how Haar Cascades and OpenCV can be used to detect objects.
In this tutorial, we will be building a simple Python script that deals with detecting human faces in an image, we will be using Haar Cascade Classifiers in OpenCV library.
In this Computer Vision and OpenCV Tutorial in C++, I'll talk about Object Detection with Haar Cascade Classifiers. We will first talk about the theory behin...
Learn Free how to create a virtual pen and eraser with python and OpenCV with source code and complete guide. This entire application is built fundamentally on contour detection. It can be thought of as something like closed color curves on compromises that have the same color or intensity, it's like a blob. In this project we use color masking to get the binary mask of our target color pen, then we use the counter detection to find the location of this pen and the contour to find it.