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We present a real-time algorithm to automatically classify the 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 and computes its motion model. This model is combined with global crowd characteristics to compute the movement patterns and motion dynamics and use them for crowd prediction. Our learning scheme is general and we highlight its performance in identifying the personality of different pedestrians in low and high density crowd videos. We also evaluate the accuracy by comparing the results with a user study.
Bera et al. (Fri,) studied this question.