How to efficiently realize automatic r ecognition of abnormal behavior for intelligent video surveillance is a key problem.A method has been developed that the dynamic particle flow field from video is got based on the Lagrange dynamic system equation and self-adaptive determination of time interval in the equation.Some motion behaviors for motion objects in video are mapped to the dynamic particle flows w hich can be used to describe their motion variation states.Some significant motion features for abnormal behavior with different motion styles from different scenes have been extracted to classify and recognize the abnormal beha viors.Some open video test sequences from different scenarios with different behavioral patterns are selected to perf orm experimental verifications and comparisons.Experimental results show that abnormal behavior can be automaticall y recognized efficiently in various conditions where it is not necessary to track motion object or collect abnorm al behavior sample in advance for learning and training.