Slip and Fall Detection using Spatiotemporal Characteristics of Human Object for Video Surveillance System

Abstract

This paper presents a method to detect a slip event and a fall event by computing integrated spatiotemporal energy (ISTE) map. ISTE map includes motion and time of motion occurrence as our motion feature. The extracted human shape is represented by an ellipse that provides crucial information of human motion activities. We use this features to detect the events in the video with non-fixed frame rate. This work assumes that the person lies on the ground with no or little motion after the fall accident. For indoor and the outdoor scenes with different illuminate conditions, we need to adjust the threshold of the background subtraction and the parameters of the smoothing filter independently. Experimental results show that our method is effective for fall and slip detection. The total number of testing frames is about 2.7×106 , and we use an Intel Core2 Duo 1.8GHz CPU on a Microsoft Windows XP operating system.