HOG Feature Extraction MATLAB Function

This HOG feature extraction function serves as a critical component in pedestrian detection systems. The function extracts distinctive pedestrian features from input images, enabling effective human detection. HOG (Histogram of Oriented Gradients) is a widely adopted computer vision technique that captures object shape and texture characteristics by computing gradient orientation histograms across image regions. The MATLAB implementation typically involves several key steps: dividing the input image into small connected regions (cells), calculating gradient magnitudes and orientations for each pixel, creating orientation histograms for each cell, grouping cells into larger blocks for contrast normalization, and finally concatenating all block histograms to form the feature vector. This process enhances robustness to illumination changes and minor geometric transformations. Through this HOG feature extraction function, we can achieve more accurate identification and detection of pedestrians in images, significantly improving both the precision and efficiency of pedestrian detection systems. The function typically returns a feature vector that can be directly fed into classifiers like SVM for pedestrian recognition tasks.