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We present a real-time algorithm to automatically classify the dynamic behavior or personality of a pedestrian based on his or her movements in a crowd video. Our classification criterion is based on Personality Trait Theory. We present a statistical scheme that dynamically learns the behavior of every pedestrian in a scene and computes that pedestrian’s motion model. This model is combined with global crowd characteristics to compute the movement patterns and motion dynamics, which can also be used to predict the crowd movement and behavior. We highlight its performance in identifying the personalities of different pedestrians in lowand high-density crowd videos. We also evaluate the accuracy by comparing the results with a user study.