Abnormal behavior of pedestrian detection based on fuzzy theory

To automatically identify pedestrian abnormal movement in Intelligent Monitoring System, a simplified articulated model of human body is presented. A fuzzification function using the variety in trunk and limbs contour angles of pedestrian is designed. Then an abnormal behavior discrimination algorithm based on fuzzy theory is proposed. The algorithm applies fuzzy membership of the trunk and limbs of pedestrian to get the overall degree of anomaly. In the system realization, a method of combining center of mass trajectory and fuzzy discriminant is proposed to discriminate the anomaly of pedestrian. Fuzzy discriminant can implement active analysis of pedestrian behavior in visual surveillance and thereby detect irregularities to recognize abnormal behavior and alarm. The experimental results show that the proposed algorithm has a higher recognition rate.