Analysis of Walking and Running Based on Markerless Model

This research investigated the possibility of side view human gait silhouette to be used for recognition of walking and running gait based on model-based approach. Markerless model with model based is used to produce the vertical angles of both hip and knee with respect to thigh for 32 image sequences as feature vectors for both legs for one complete cycle sequences. Overall, a total of 128 features are extracted based on four parameters from the lower limb of human body are validated for walking speed classification purpose. Further, the gait features extracted from different gait speeds is classified as walking and running gait using ANN and KNN. Initial findings with accuracy of almost 100% confirmed that the proposed method suited to be utilized as walking speed classification based on human gait.