Title :
A viewpoint-independent statistical method for fall detection
Author :
Zhong Zhang ; Weihua Liu ; Metsis, Vangelis ; Athitsos, V.
Author_Institution :
Univ. of Texas At Arlington, Arlington, TX, USA
Abstract :
The goal of a fall detection system is to automatically detect cases where a human falls and may have been injured. We propose a statistical method based on Kinect depth cameras, that makes a decision based on information about how the human moved during the last few frames. Our method proposes novel features to be used for fall detection, and combines those features using a Bayesian framework. Our experiments explicitly evaluate the ability of our method to use training data collected from one viewpoint, in order to recognize falls from a different viewpoint. We obtain promising results, on a challenging dataset, that we have made public, and that contains, in addition to falls, several similar-looking events such as sitting down, picking up objects from under the bed, or tying shoelaces.
Keywords :
Bayes methods; cameras; image motion analysis; statistical analysis; Bayesian framework; Kinect depth cameras-based statistical method; automatic fall detection system; information-based decision; training data; viewpoint-independent statistical method; Accuracy; Cameras; Feature extraction; Head; Sensors; Training; Training data;
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
Print_ISBN :
978-1-4673-2216-4