HOG + LBP + HIKSVM Pedestrian Detection Algorithm

This document provides MATLAB code implementation for a pedestrian detection algorithm based on HOG (Histogram of Oriented Gradients), LBP (Local Binary Patterns), and HIKSVM (Histogram Intersection Kernel Support Vector Machine), along with the included libsvm-mat-3.0-1 package. The algorithm is widely used in computer vision applications, particularly in intelligent security systems. By implementing this algorithm, we can significantly enhance pedestrian detection efficiency in video surveillance systems. The implementation relies on various parameters and variables including image size, feature extraction methods, classifier selection, and training data configuration. The code structure includes key functions for HOG feature computation using gradient magnitude and orientation bins, LBP texture feature extraction through local pattern encoding, and HIKSVM classification with histogram intersection kernel optimization. To optimize algorithm performance, users should understand the meaning of these parameters and how to configure them properly. We recommend users conduct their own testing and experiments to better evaluate the algorithm's performance characteristics and limitations under different scenarios.