Human Shape Tracking for Gait Recognition Using Active Contours with Mean Shift

Abstract

In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean-shift algorithm. The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property, but they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean-shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level-set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe object. This mechanism allows for reducing the number of iterations and for handling the large object motion. Our system is composed of human region detection and human shape tracking. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean-shift algorithm. To evaluate the effectiveness of the proposed method, it is applied the common gait data, then the results show that the proposed method is extracted and tracked efficiently accurate shape for gait recognition.