Quickest change detection for health-care video surveillance
Detecting changes in video scenes is of fundamental importance for various video surveillance tasks. Of particular interest are abnormal changes of foreground human behaviors/activities that could pose damages or dangers to human properties and lives. In this paper, we propose a unified sequential approach to detecting, as soon as possible, human fall incidents for health-care purpose. Specifically, aspect ratio of human body is extracted as the representative feature, based on which an event-inference module parses observed feature sequences for possible falling behavioral signs. Experimental results are reported to show the efficacy of the proposed approach