Fall detection based on posture classification for smart home environment

摘要

Automatic human fall detection is one of the major components in the elderly monitoring system. Detection of human fall in a smart home environment can be utilized as a means to improve the quality of care offered to elderly people thus reducing the risk factor when they are alone. Recently various fall detection approaches have been proposed, among which computer vision based approaches offer promising and effective solutions. In this paper, an analysis of fall detection is carrier out based on the automatic, feature learning in a hybrid approach. Initially, a model is generated using the training dataset that contains samples of both fall and normal active events. Then key frames are extracted from the video sequence that is subjected to two stream classification. The classification results are approved if both the streams project the same results, failing so, additional information are used to classify the fall from the normal activity. The selection of key frames depends on the displacement in the centroid of the detected object have threshold greater than the predefined value. Experiments show that the proposed approach achieves reliable results compared with other methods, and a better result is achieved in our method even when training with fewer training samples.