Human fall detection using slow feature analysis

Falls are reported to be the leading causes of accidental deaths among elderly people. Automatic detection of falls from video sequences is an assistant technology for low-cost health care systems. In this paper, we present a novel slow feature analysis based framework for fall detection in a house care environment. Firstly, a foreground human body is extracted by a background subtraction technique. After morphological operations, the human silhouette is refined and covered by a fitted ellipse. Secondly, six shape features are quantified from the covered silhouette to represent different human postures. With the help of the learned slow feature functions, the shape feature sequences are transformed into slow feature sequences with discriminative information about human actions. To represent the fall incidents, the squared first order temporal derivatives of the slow features are accumulated into a classification vector. Lastly, falls are distinguished from other daily actions, such as walking, crouching, and sitting, by the trained directed acyclic graph support vector machine. Experiments on the multiple-camera fall dataset and the SDUFall dataset demonstrate that our method is comparable to other state-of-the-art methods, achieving 94.00% recognition rate on the former dataset and 96.57% on the latter one.