Crowd counting based on Difference Images

Jinyan Chen

doi:10.5755/j01.eee.19.2.3475

Abstract

Many crowd counting methods were proposed in recently years. Most of these methods were implemented by extracting the human silhouette from the background image. But under some conditions it is difficult get a clear background image. In this paper a crowd counting method based on the images difference is proposed, instead of extract silhouette from background, the surveillance was divided into frames. Difference image of two frames is calculated by images subtraction. Then image features were extracted based on difference image and the crowed count is calculated based on these features. Experiment result show that this method is feasible. DOI: http://dx.doi.org/10.5755/j01.eee.19.2.3475

Highlights

It is necessary to control the crowd size in public place in order to avoid overcrowding or for other security reason
The difficulty of estimating the crowd size comes from three ways: 1) There is often overlapping among the pedestrians
To estimate the crowd size several features are extracted from the difference image

Summary

INTRODUCTION

It is necessary to control the crowd size in public place in order to avoid overcrowding or for other security reason. In the past years researchers are try to find ways to estimate the crowd from the surveillance equipment automatically. The difficulty of estimating the crowd size comes from three ways: 1) There is often overlapping among the pedestrians. Several methods have been developed to estimate the size of crowd in the past years. Ryan [1] use foreground pixels and other local features to estimate the crowd size; Chan [2] counting the Pedestrians by segment the crowd into components of homogeneous motion; Kong [3] using background subtraction and edge detection to each frame and extracting edge orientation and blob size histograms as features and using these features to estimate the crowd size. Huang [5] detect heads from the stereo image by scale-adaptive filtering and calculate the crowd size

About the difference image

OUR APPROACH

Feature extraction from the difference image

Perspective normalization

Method

CONCLUSIONS