Crowd Density Estimation Based on Local Binary Pattern Co-Occurrence Matrix

Crowd density estimation is important for intelligent video surveillance. Many methods based on texture features have been proposed to solve this problem. Most of the existing algorithms only estimate crowd density on the whole image while ignore crowd density in local region. In this paper, we propose a novel texture descriptor based on Local Binary Pattern (LBP) Co-occurrence Matrix (LBPCM) for crowd density estimation. LBPCM is constructed from several overlapping cells in an image block, which is going to be classified into different crowd density levels. LBPCM describes both the statistical properties and the spatial information of LBP and thus makes full use of LBP for local texture features. Additionally, we both extract LBPCM on gray and gradient images to improve the performance of crowd density estimation. Finally, the sliding window technique is used to detect the potential crowded area. The experimental results show the proposed method has better performance than other texture based crowd density estimation methods.

I. INTRODUCTION

With the growth of population and the worldwide urbanization, crowd phenomenon has become more and more frequent. Crowd density estimation is an important issue in intelligent video surveillance, which becomes an important research approach of computer vision in recent years. Polus et al.[1] first introduce the level of services for a pedestrian flow, which is widely adopted. Based on this idea, crowd density can be defined as: free flow, restricted flow, dense flow and jammed flow. In real world surveillance application, different crowd density levels maybe need different attention.