HOG-LBP+Detection Pedestrian Detection Algorithm

HOG-LBP+detection is an advanced pedestrian detection technique that leverages the combined strengths of Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) feature extraction methods. The HOG algorithm computes gradient orientation histograms across dense image grids, effectively capturing object contours and shape characteristics through cell-based normalization. Meanwhile, the LBP algorithm performs texture analysis by comparing pixel intensities with their neighbors, generating rotation-invariant patterns that describe local texture features. By integrating these complementary descriptors, HOG-LBP+detection achieves robust pedestrian identification through feature concatenation and classifier training (typically using SVM or AdaBoost). In implementation, the algorithm processes input images through multi-scale sliding windows, extracts combined HOG-LBP feature vectors, and applies pre-trained models for classification. This technology finds extensive applications in video surveillance systems, intelligent transportation solutions, and autonomous driving platforms where reliable human detection is critical.