Human activity recognition from basic actions using graph similarity measurement

Human activity recognition has an important role for the automatic anomaly event detection and recognition application such as surveillance system and patient monitoring system. In this paper, we propose a human activity recognition method based on graph similarity measurement technique (GSM). The basic actions with their movements for each person in the interested area are extracted and calculated. The action sequence with movement features of labelled dataset are used as basis data to establish the statistical activity graph model that used to calculate similarity between graphs. The system performs good results, (sensitivity and specificity are about 80% for first testing activity and about 90% for second testing activity).