Pedestrian Detection using HOG Features and AdaBoost Algorithm

This MATLAB implementation of pedestrian detection utilizes the HOG (Histogram of Oriented Gradients) feature extraction method combined with the AdaBoost classification algorithm. The system requires substantial training and testing image datasets for proper operation. Pedestrian detection represents a crucial computer vision task that accurately identifies and localizes pedestrians' positions and poses within images or video streams. The implementation employs HOG features to capture pedestrian shape and texture characteristics effectively. In MATLAB code, this typically involves using functions like extractHOGFeatures() to compute gradient orientations and magnitudes across image cells. The HOG descriptor creates a robust representation of pedestrian appearance by analyzing local gradient patterns. The AdaBoost algorithm serves as the classification component, combining multiple weak classifiers into a strong ensemble classifier. MATLAB's fitensemble() function with the 'AdaBoostM1' method can be employed to train the classifier, which iteratively focuses on misclassified samples to improve detection accuracy. The training process requires extensive positive (pedestrian) and negative (non-pedestrian) image samples. Implementation typically involves preprocessing images, extracting HOG features, training the AdaBoost classifier, and then applying the trained model to new images using a sliding window approach with functions like imresize() and blockproc() for multi-scale detection. While pedestrian detection demands significant computational resources and time, MATLAB's comprehensive image processing toolbox (including functions like vision.PeopleDetector and image processing functions) facilitates efficient implementation, algorithm optimization, and debugging. The implementation may include performance evaluation metrics such as precision-recall curves and detection error tradeoff analysis to validate system effectiveness.