Classification of human fall in top Viewed kinect depth images using binary support vector machine

Abstract

Vision based human fall action classification from non fall has been given significant importance over the past decade since the rise of falling events related to elderly people living alone has increased. This paper proposes a method to classify falls from non fall action in top Viewed kinect camera depth images. The usage of depth camera images provides an effective solution regarding privacy concerns and the top Viewed camera output has an added advantage of reducing occlusion effect in the cluttered home environment. Our method considers a fixed background setting overall the experiments and foreground is obtained by frame differencing. Then the human silhouette is extracted by largest connected component selection. Ellipse Fit over the human silhouette is used to obtain feature vectors. A binary support vector machine(SVM)classifier is used to distinguish fall from non falling frames. The proposed method is tested over[6] UR fall detection dataset providing a platform for comparison to other researchers.