MATLAB Implementation of Pedestrian Detection Combining HOG and LBP Features

In the field of computer vision, pedestrian detection represents a classical problem, where HOG (Histogram of Oriented Gradients) and LBP (Local Binary Pattern) are two commonly used feature description methods. Combining these two features can enhance detection robustness, particularly in scenarios with complex backgrounds and varying lighting conditions. ### Introduction to HOG Features HOG features describe structural characteristics of targets by calculating gradient orientation distributions in local image regions. They exhibit strong robustness to human body contours and posture variations, making them widely adopted in pedestrian detection. The core concept involves statistically analyzing gradient directions of individual pixels, then performing histogram statistics through block and cell divisions to form the final feature vector. In MATLAB implementation, the extractHOGFeatures function calculates gradient magnitudes and orientations, then constructs spatial histograms using specified cell sizes and block normalization. ### Introduction to LBP Features LBP features serve as texture descriptors that generate binary patterns by comparing pixel values with their neighborhood grayscale values. LBP features demonstrate good invariance to illumination changes and effectively capture local texture information. For pedestrian detection, LBP compensates for HOG's limitations in texture details, especially in scenarios involving clothing textures or complex background interference. MATLAB's extractLBPFeatures function implements uniform LBP patterns that reduce feature dimensionality while maintaining discriminative power. ### Integration of HOG and LBP Implementing HOG-LBP combined pedestrian detection in MATLAB typically follows this approach: Feature Extraction: Compute HOG features using extractHOGFeatures with parameters like cell size [8 8] and block size [2 2]. Calculate LBP features through extractLBPFeatures specifying radius and neighborhood points (e.g., radius=1, neighbors=8). Feature Fusion: Concatenate HOG and LBP feature vectors to form joint features. Normalize both features using z-score or min-max normalization to ensure scale consistency before vector concatenation. Classifier Training: Employ SVM (Support Vector Machine) or other machine learning algorithms for classification. MATLAB's fitcsvm function trains linear or RBF kernel SVMs with cross-validation for parameter optimization. Detection and Validation: Perform sliding window detection on test images using the trained model, followed by Non-Maximum Suppression (NMS) to eliminate redundant detection boxes. The detectPeople function can be customized to integrate both feature types. ### Advantages and Challenges Advantages: HOG+LBP combination leverages both contour and texture information, improving detection accuracy particularly under uneven lighting or complex backgrounds. Challenges: High feature dimensionality may increase computational load, requiring feature selection optimization or dimensionality reduction methods like PCA (Principal Component Analysis). MATLAB's pca function can reduce feature dimensions while preserving variance. Through appropriate parameter tuning and optimized feature fusion strategies, MATLAB-implemented HOG+LBP pedestrian detection can maintain high accuracy while adapting to diverse real-world scenarios. Code implementation typically involves preprocessing steps like image resizing, multi-scale detection pyramids, and post-processing with overlap thresholding in NMS.