In this paper, we present an approach for human fall de- tection, 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 con- tinuously monitor people and their activities. Experimental results show that our method can detect all possible types of human fall accurately and successfully.