Anomaly Detection in Crowded Scenes: A Novel Framework Based on Swarm Optimization and Social Force ModelingModeling, Simulation and Visual Analysis of Crowds

This chapter presents a novel scheme for analyzing the crowd behavior from visual crowded scenes. The proposed method starts from the assumption that the interaction force, as estimated by the Social Force Model (SFM), is a significant feature to analyze crowd behavior. We step forward this hypothesis by optimizing this force using Particle Swarm Optimization (PSO) to perform the advection of a particle population spread randomly over the image frames. The population of particles is drifted towards the areas of the main image motion, driven by the PSO fitness function aimed at minimizing the interaction force, so as to model the most diffused, normal behavior of the crowd. We then use this proposed particle advection scheme to detect both global and local anomaly events in the crowded scene. A large set of experiments are carried out on public available datasets and results show the consistent higher performances of the proposed method as compared to other state-of-the-art algorithms.