A human gait classification method based on adaboost techniques using velocity moments and silhouette shapes

Abstract

In this paper, we propose a human gait classification method based on Adaboost techniques that identify three kinds of human gaits: walk, run, and limp. We divide a video sequence into several segments, each of which is regarded as a process unit. For each process unit, we collect both the velocity and shape information of a moving object. We first apply the Canny edge detector to enhancing the edges within a foreground image, followed by computing the distance and the angle difference between two edge pixels which are put into the accumulation table. The gait classification employs an Adaboost algorithm which is excellent in facilitating the speed of convergence during the training. The experimental result reveals that our method has good performance of classifying human gaits using both the velocity and shape information. © 2009 Springer Berlin Heidelberg.