The Large-Scale Crowd Behavior Perception Based on Spatio-Temporal Viscous Fluid Field

Over the past decades, a wide attention has been paid to crowd control and management in the intelligent video surveillance area. Among the tasks for automatic surveillance video analysis, crowd motion modeling lays a crucial foundation for numerous subsequent analysis but encounters many unsolved challenges due to occlusions among pedestrians, complicated motion patterns in crowded scenarios, etc. Addressing the unsolved challenges, the authors propose a novel spatio-temporal viscous fluid field to model crowd motion patterns by exploring both appearance of crowd behaviors and interaction among pedestrians. Large-scale crowd events are hereby recognized based on characteristics of the fluid field. First, a spatio-temporal variation matrix is proposed to measure the local fluctuation of video signals in both spatial and temporal domains. After that, eigenvalue analysis is applied on the matrix to extract the principal fluctuations resulting in an abstract fluid field. Interaction force is then explored based on shear force in viscous fluid, incorporating with the fluctuations to characterize motion properties of a crowd. The authors then construct a codebook by clustering neighboring pixels with similar spatio-temporal features, and consequently, crowd behaviors are recognized using the latent Dirichlet allocation model. The convincing results obtained from the experiments on published datasets demonstrate that the proposed method obtains high-quality results for large-scale crowd behavior perception in terms of both robustness and effectiveness.