Automatic Detection of Human Fall in Video

In this paper, we present an approach for human fall detection, which has an important application in the field of safety and security. The proposed approach consists of two part: object detection and fall model. We use an adaptive background subtraction method to detect moving object and mark it with minimum-bounding box. Fall model uses a set of extracted features to analyze, detect and confirm the fall. We implement a two-state finite state machine (FSM) to continuously monitor people and their activities. Experimental results show that our method can detect all possible types of human fall accurately and successfully.