An SVM Classifier with MATLAB Implementation

An SVM (Support Vector Machine) classifier is a widely-used machine learning algorithm extensively applied in classification tasks, including pedestrian detection. Its core principle involves finding an optimal hyperplane that maximizes the margin between different classes to achieve effective classification.

In pedestrian detection applications, SVM is typically employed to distinguish positive samples (pedestrians) from negative samples (background or other objects). Training samples usually consist of feature vectors extracted through descriptors like HOG (Histogram of Oriented Gradients) or Haar features. Once trained, the SVM model can classify new input samples.

MATLAB provides convenient SVM implementation through functions like `fitcsvm` for classifier training and `predict` for model inference. Users simply need to prepare labeled training data and select appropriate kernel functions (such as linear or RBF kernels) to quickly build classification models. The `fitcsvm` function automatically handles parameter optimization while allowing customization of kernel types and regularization parameters.

This approach is widely adopted in computer vision domains, particularly suitable for real-time applications like intelligent surveillance systems and autonomous driving where computational efficiency is crucial. The algorithm's strong generalization capability makes it effective even with limited training data.