A novel method is developed to automatically recognize invasion abnormality based on a 3D virtual space,aiming at some limits in abnormal invasion behavior recognition fo r pedestrian in a video surveillance scenario at present.A 3D plane equation is constructed according to a sing le pedestrian extracted and tracked by his head in a video surveillance scenario.A corresponding 3D virtual warning sp ace in the surveillance scene is built. The problem whether a pedestrian invades warning region in a 2D scene is transfo rmed to the one whether he intrudes the 3D virtual warning space.A sliding filter statistics strategy in ray projec tion of the pedestrian′s head is developed to identify whether the pedestrian i nvades the warning protective region or not.Neither any regular shape constraint for the specified warning region or scene content previous learning is considered in the proposed approach.Some state-of-the-arts and experiments are done in some vid eo scenes with different contents to test the performance of the proposed method in the same conditions.Experimental resul ts show that the developed method is efficient and valid without any specific hardware support or conditional constraint f or the scenario.