HOG-Based Pedestrian Detection Program with MATLAB Implementation

This MATLAB-based pedestrian detection program utilizes Histogram of Oriented Gradients (HOG) features for robust pedestrian identification. While the README documentation provides extensive implementation details, we can further elaborate on its underlying methodology. The program begins by extracting HOG features from input images, which effectively capture pedestrian shape and texture characteristics through gradient orientation histograms. The implementation involves computing gradient magnitudes and orientations across image cells, then forming histogram descriptors. These features are fed into a Support Vector Machine (SVM) classifier trained to distinguish pedestrians from non-pedestrian objects. For detection, the program employs a sliding window technique that systematically scans the entire image at multiple scales, using the trained SVM to classify each window region. The algorithm incorporates non-maximum suppression to eliminate duplicate detections. This efficient and practical implementation demonstrates rapid and accurate pedestrian detection capabilities, making it valuable for both research applications and practical computer vision projects.