Human Gait Silhouettes Extraction Using Haar Cascade Classifier on OpenCV

Abstract

Human Silhouette Extraction is one of the vital task in human abnormality gait detection. Hence in this study, extraction of human gait silhouette using Haar Cascade classifier is developed. This is done by detecting the region of interests (ROI) and by cropping the selected areas followed by background subtraction to eliminate unwanted background and further convert the images to silhouettes form based on human gait sequences acquired. Next, human abnormality gait detection is conducted based on front view markerless model. Classification results attained showed that the developed Haar cascade classifier is indeed suitable and robust in extracting front view gait features for abnormality gait detection with detection time of KNN surpass the SVM classifier.