OpenCV Haar Cascades

OpenCV Haar Cascades

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:

  1. An important part of the computer vision and image processing literature
  2. Still used with OpenCV
  3. Still useful, particularly when working in resource-constrained devices when we cannot afford to use more computationally expensive object detectors

In the remainder of this tutorial, you’ll learn about Haar cascades, including how to use them with OpenCV.

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