Fall Incidents Detection for Intelligent Video Surveillance

We present in this paper an intelligent video surveillance system to detect human fall incidents for enhanced safety in indoor environments. The system consists of two main parts: a vision component which can reliably detect and track moving people in the view of a camera, and an event-inference module which parses observation sequences of people features for possible falling behavioral signs. In particular, we extract the aspect ratio of a person as observation feature, based on which fall incidents are detected as abrupt changes in the feature space. Our experiments show that the proposed approach can robustly detect human falls in real time.