Pedestrian Detection using Haar Features and SVM

This MATLAB-implemented pedestrian detection code collection based on Haar features and Support Vector Machine (SVM) serves as a highly practical tool for accurate pedestrian identification in images and videos. The implementation extracts Haar-like features through integral image computation for efficient feature calculation, then employs SVM classifier training with histogram of oriented gradients (HOG) descriptors for robust pattern recognition. The package includes complete algorithm code alongside a curated dataset for immediate testing and model training. Users can seamlessly integrate pedestrian detection capabilities into their projects, significantly reducing development time and effort. The algorithm's foundation on Haar features ensures efficient multi-scale feature extraction, while SVM provides superior classification accuracy and stability. The system delivers satisfactory performance for both real-time pedestrian detection in video streams and static image analysis, featuring adaptive thresholding and non-maximum suppression for optimized detection results. The code includes preprocessing modules for image enhancement, Haar feature selection algorithms, and post-processing functions for false positive reduction.