Abnormal crowd behavior detection based on the energy model

Abstract

In this paper, we present a novel method to detect two typical abnormal activities: pedestrain gathering and running. The method is based on the potential energy and kinetic energy. Reliable estimation of crowd density and crowd distribution are firstly introduced into the detection of anomalies. Estimation of crowd density is obtained from the image potential energy model. By building the foreground histogram on the X and Y axis respectively, the probability distribution of the histogram can be obtained, and then we define the Crowd Distribution Index (CDI) to represent the dispersion. The Crowd Distribution Index (CDI) is used to detect pedestrains gathering. The kinetic energy is determined by computation of optical flow and Crowd Distribution Index, and then used to detect people running. The detection for abnormal activities is based on the threshold analysis. Without training data, the model can robustly detect abnormal behaviors in low and medium crowd density with low computation load.