SVM-based abnormal activity detection for home care

Abnormal activity detection for intelligent home care is presented in this paper. The activities have been catalogued into six possible classes, such as standing, sitting, squatting, walking, jogging, and falling down, among which falling down including on-marching falling down and in-place falling down is regarded as the abnormal activity. The output of background subtraction is employed directly to obtain the binary human-body images and only centroid track and figure width of human blob are selected as features for recognition. Activities are sub-divided into moving activities and quasi-static activities in terms of the horizontal movement of the centroid of body blob. And then SVM classifiers are used to recognize respectively the on-marching falling down and in-place falling down from above two classes of behaviors. A home-brewed activity database is obtained and the experimental results are: the correct identification rate is 100 percent for on-marching falling down and the minimum correct identification is above 90 percent for in-place falling down activity.