The object recognition technique, Haar cascade, based on machine learning, is often used to find and identify objects in pictures and video streams. It was first suggested in 2001 by Viola and Jones, and it is called after the Haar wavelets that it uses to extract features.
Explain Haar Cascade
An explanation of the Haar cascade, ranging from fundamentals to complex ideas.
- Haar-like features: To represent patterns in a picture, the method first uses Haar-like features. Rectangular characteristics that resemble Haar's are easily calculated. They record subtle contrast fluctuations in the image's borders, corners, and texture.
- Integral image: An integral picture is computed to expedite the feature computing process. A simplified version of the original picture, the integral image enables quick calculation of rectangular characteristics at multiple sizes and places. It is made by adding the intensities of the pixels in a manner that makes it possible to calculate the total pixel values in any rectangular area quickly.
- Building a strong classifier: The Haar cascade algorithm builds a robust classifier using the machine learning method known as AdaBoost (Adaptive Boosting). Both positive samples, which include the object of interest, and negative samples, which contain non-object regions, are employed during training.
- Haar-like feature selection: From a vast pool of candidate features, AdaBoost repeatedly chooses the most informative Haar-like features. Higher weights are assigned to the traits that can more effectively discriminate between positive and negative samples.
- Constructing a strong classifier: A strong classifier combines many weak classifiers based on a distinct Haar-like feature. Weighted voting combines the weak classifiers, where classifiers with higher weights significantly impact the outcome.
- Classifier cascade: The powerful classifier is set up in phases. Each level has several ineffective classifiers. The cascade's effective rejection of non-object areas at an early stage of the detection process lowers the computing burden. The method gradually reduces the search space for prospective items as the picture is processed via the cascade.
- Sliding window detection: The Haar cascade technique uses a sliding window method to scan a picture in various sizes and places. The cascade analyses the presence or absence of the item of interest based on the learned classifiers at each location and scale. If the object's existence is discovered, it is further identified and localized.
- Object recognition and detection: The algorithm produces a list of probable object detections after thoroughly scanning the whole picture. Using methods like non-maximum suppression, these detections are improved to remove redundant or overlapping ones and choose the most reliable ones.
The Haar cascade object detection approach has proven reliable and effective in several applications, such as face identification, pedestrian detection, and object tracking. It is helpful for a wide range of real-world applications because of its real-time image processing capabilities.
What is the Difference between CNN and Haar Cascade?
Although both CNN (Convolutional Neural Network) and Haar cascade object-to-identification methods, their underlying algorithms and strategies vary. The following are some major distinctions between CNN and Haar cascade:
|Design||CNN is a deep learning algorithm created primarily for visual data analysis. Convolutional, pooling and completely linked layers comprise this structure's many interconnected neurons .||The Haar cascade machine learning technique recognizes objects using a series of simple classifiers, sometimes called Haar-like features.|
|Feature extraction||CNN uses its convolutional layers to extract features automatically. It learns to extract hierarchical features from the input data that may capture intricate patterns and spatial correlations.||On the other hand, the handmade Haar-like features used by Haar cascade are intended to capture straightforward visual patterns like edges, corners, and texture changes.|
|Training process||The training method for CNN needs a large, labeled dataset. Iterative learning determines the best weights to apply to reduce classification errors.||Haar cascade chooses and combines the most informative Haar-like characteristics using the AdaBoost algorithm. It builds a strong classifier by combining weak classifiers that have been trained repeatedly.|
|Flexibility and generalization||CNN is renowned for its capacity to handle complicated visual images and generalize effectively across many object identification tasks. It can pick up a variety of characteristics and adjust to different object shapes, sizes, and lighting situations.||While useful in certain areas (such as face identification), the Haar cascade may have trouble with size, rotation, and occlusion differences. Its reliance on manually built features constrains its flexibility and generalization abilities.|
|Computational effectiveness||CNN requires more processing, particularly for large-scale networks. CNN training and operation often need a lot of computer power.||The computational effectiveness of the Haar cascade makes it appropriate for real-time applications. It swiftly eliminates non-object areas using integral pictures and a cascaded classifier framework.|
|Training data requirements||CNN needs a lot of labeled training data to understand the intricate patterns and variances in things, which brings us to number six. End-to-end training is used to optimize feature extraction and classification simultaneously.||Due to its constructed features, the Haar cascade may provide respectable results with a smaller training dataset while still needing positive and negative examples.|
In general, CNN is a strong and adaptable deep learning method that performs very well in various computer vision applications, including object identification. It immediately picks up hierarchical characteristics and is flexible in various situations. In contrast, the lighter and more effective Haar cascade technique is better suited for applications with more straightforward visual patterns, such as face identification.