Classical Pedestrian Detection Algorithm

This document discusses a classical pedestrian detection algorithm implemented using Histogram of Oriented Gradients (HOG) features and Support Vector Machine (SVM) classification. The algorithm has been thoroughly debugged and operates correctly. Pedestrian detection represents a crucial research direction in computer vision, designed to identify pedestrian targets in images or video streams. HOG is a feature descriptor that extracts local image features to characterize pedestrian shape and texture information. The implementation typically involves computing gradient magnitudes and orientations across image cells, creating histograms, and normalizing blocks for illumination invariance. SVM serves as the machine learning component that learns a classification model from training samples to distinguish between pedestrian and non-pedestrian targets. The algorithm employs a linear SVM classifier trained on positive (pedestrian) and negative (non-pedestrian) samples, achieving optimal separation through maximum margin hyperplane calculation. Practical testing demonstrates that this pedestrian detection algorithm maintains robust performance and accuracy across diverse scenarios. The implementation includes multi-scale detection windows and non-maximum suppression to handle varying pedestrian sizes and eliminate duplicate detections.