Human behavior analysis is an important challenge in many domains, such as surveillance systems, video content retrieval, human interactive systems, medical diagnosis, etc. With the increasing needs of public safety, intelligent surveillance system becomes an activating issue in computer vision and related research fields. In this thesis we present a method to analyze human behavior in a video sequence with depth information obtained from the depth camera. When interested actions are detected in the scene, the system will trigger alarm information. Contour line and Delaunay triangulation are used to establish human posture model. By traversing the triangulation meshes with the depth first search, we obtain the spanning tree with the depth information, and then construct human posture model with this spanning tree. Posture sequence from video sequence with corresponding posture models can be obtained, and then the posture sequences is clustered into key posture sequence. By querying the key posture sequence, the system can recognize human behavior in real-time and inform users immediately when interested actions detected. Experimental results show that the system is accurate and robust for human behavior recognition.