CascadeClassifier

CascadeClassifier is a class in the OpenCV computer vision library used for object detection. It implements the Viola-Jones object detection framework, which is a machine learning-based approach. This framework uses a cascade of Haar-like features to detect objects in an image. Initially, the framework was designed for face detection, but it can be trained to detect other objects as well.

The core idea behind CascadeClassifier is to efficiently scan an image for a specific object. It utilizes a series of simple rectangular features that can be computed quickly. These features are then combined into a cascade of classifiers. Early stages of the cascade are trained to reject non-object regions with high accuracy, while later stages are more complex and are used to confirm the presence of an object. This cascaded approach allows for rapid rejection of most of the image, significantly speeding up the detection process.

To use CascadeClassifier, one typically needs a pre-trained XML file that contains the learned features and classifier stages for the object of interest. This file is loaded into the CascadeClassifier object. The detectMultiScale method is then used to scan the input image. This method returns a list of rectangles, each representing a detected object. Parameters such as scaleFactor and minNeighbors can be adjusted to fine-tune the detection sensitivity and reduce false positives. While effective, CascadeClassifier is sensitive to rotation and scale variations if the training data did not adequately cover these.