/Haar-Cascade-Full-Body-Classifier

Haar Cascade is a machine learning object detection algorithm used to identify objects in an image or a video. It is well known for being able to detect faces and body parts in an image, but can be trained to identify almost any object. In this use case, haar cascade classifier is used to detect full body using OpenCV.

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Haar Cascade Full Body Classifier

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.

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.

The algorithm has four stages:-

  1. Haar Feature Selection
  2. Creating Integral Images
  3. Adaboost Training
  4. Cascading Classifiers

It is well known for being able to detect faces and body parts in an image, but can be trained to identify almost any object.

In this use case, haar cascade classifier is used to detect full body using OpenCV.

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