HOG Feature Extraction and SVM Classification for Person/Vehicle Background Recognition

In this document, we explore how to implement HOG (Histogram of Oriented Gradients) feature extraction and SVM (Support Vector Machine) classification methods using MATLAB to distinguish between persons/vehicles and background. HOG is a feature descriptor that computes gradient orientation histograms for local image regions, widely used in computer vision for object detection and recognition tasks. SVM is a supervised learning algorithm that builds classification models by learning from training samples' features and labels, enabling classification of new unknown samples. We combine these two methods to achieve effective classification and recognition of persons/vehicles versus background. The implementation typically involves several key steps: First, we extract HOG features using MATLAB's `extractHOGFeatures` function, which calculates gradient magnitudes and orientations across image cells, then forms histograms normalized within blocks. Key parameters include cell size (typically 8x8 pixels), block size (e.g., 2x2 cells), and the number of orientation bins (usually 9). For SVM classification, we utilize MATLAB's `fitcsvm` function to train a binary classifier. The training process involves feeding HOG features from labeled images (positive samples: persons/vehicles, negative samples: background) to create a decision boundary. Important considerations include kernel selection (linear or RBF), regularization parameter C, and potential use of histogram intersection kernel for better performance with HOG features. The following sections will provide detailed implementation steps for both HOG feature extraction and SVM classification, along with recommendations for parameter tuning and optimization techniques. We'll discuss practical aspects like handling multi-scale detection, dealing with imbalanced datasets, and implementing sliding window approaches for complete image analysis.