Crowd Abnormal Event Detection Based on Sparse Coding

Abstract

Crowd feature perception is an essential step for us to understand the crowd behavior. However, as the individuals present not only the sociality but also the randomness, there remain great challenges to extract the sociality of the individual directly. In this paper, we propose a crowd feature perception algorithm based on a sparse linear model (SLM). It builds the statistical characterization of the sociality by assuming a priori distribution of the SLM. First, we calculate the optical flow to extract the motion information of the crowd. Second, we input the video motion features to the sparse coding and generate the SLM. The super-Gaussian prior distributions in SLMs build the statistical characterization of the sociality. In addition, we combine the infinite Hidden Markov Model (iHMM) statistic model to determine whether the detected event is an abnormal event. We validate our method on UMN dataset and simulate dataset for abnormal detection, and the experiments show that this algorithm generates promising result compared with other state-of-art methods.