Abnormal Prediction of Dense Crowd Videos by a Purpose–Driven Lattice Boltzmann Model

Abstract

In the field of intelligent crowd video analysis, the prediction of abnormal events in dense crowds is a well-known and challenging problem. By analysing crowd particle collisions and characteristics of individuals in a crowd to follow the general trend of motion, a purpose-driven lattice Boltzmann model (LBM) is proposed. The collision effect in the proposed method is measured according to the variation in crowd particle numbers in the image nodes; characteristics of the crowd following a general trend are incorporated by adjusting the particle directions. The model predicts dense crowd abnormal events in different intervals through iterations of simultaneous streaming and collision steps. Few initial frames of a video are needed to initialize the proposed model and no training procedure is required. Experimental results show that our purpose-driven LBM performs better than most state-of-the-art methods.

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

“…It is generally assumed that all individuals of the crowd are moving in one direction to track multiple people based on floor fields in a structured crowded scene. In unstructured crowds, participants travel in diverse directions in different spatio-temporal aspects [2]. For instance, crowds at fairs or exhibitions, stadiums, and airports are unstructured crowded scenes.…”