HOG (Histogram of Oriented Gradients) Algorithm Implementation

This is an exceptionally concise and thoroughly documented MATLAB implementation of the HOG (Histogram of Oriented Gradients) algorithm. The algorithm is widely applicable in pedestrian detection and object tracking domains. It extracts features based on gradient information within images by computing local orientation gradient histograms to characterize image features. This implementation demonstrates key computational steps including gradient computation using Sobel filters, orientation binning with 9 histogram channels (0-180 degrees), cell-based histogram formation (typically 8x8 pixel cells), and block normalization (using 2x2 cell blocks with L2-norm) to create illumination-invariant descriptors. The code efficiently handles gradient magnitude and direction calculations, histogram accumulation, and feature vector concatenation for machine learning applications. Using this algorithm enables accurate detection and recognition of pedestrians and various objects, making it an essential tool in computer vision and image processing workflows. The implementation includes visualization functions to display gradient orientations and feature descriptors for debugging and educational purposes.