Incorporating Social Entropy for Crowd Behavior Detection Using SVM

Crowd behavior analysis is a challenging task for computer vision. In this paper, we present a novel approach for crowd behavior analysis and anomaly detection in coherent and incoherent crowded scenes. Two main aspects describe the novelty of the proposed approach: first, modeling the observed flow field in each non-overlapping block through social entropy to measure the concerning uncertainty of underlying field. Each block serves as an independent social system and social entropy determine the optimality criteria. The resulted in distributions of the flow field in respective blocks are accumulated statistically and the flow feature vectors are computed. Second, Support Vector Machines are used to train and classify the flow feature vectors as normal and abnormal. Experiments are conducted on two benchmark datasets PETS 2009 and University of Minnesota to characterize the specific and overall behaviors of crowded scenes. Our experiments show promising results with 95.6% recognition rate for both the normal and abnormal behavior in coherent and incoherent crowded scenes. Additionally, the similar method is tested using flow feature vectors without incorporating social entropy for comparative analysis and the detection results indicate the dominating performance of the proposed approach.