The large-scale crowd density estimation based on effective region feature extraction method

Authors:
Hang Su;Hua Yang;Shibao Zheng
Affiliations:
Institution of Image Communication and Information Processing, Department of EE, Shanghai Jiaotong University, Shanghai, China;Institution of Image Communication and Information Processing, Department of EE, Shanghai Jiaotong University, Shanghai, China;Institution of Image Communication and Information Processing, Department of EE, Shanghai Jiaotong University, Shanghai, China
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Year:
2010

Citing 4
Cited 0

Pairwise classification and support vector machines

Advances in kernel methods
Two-Stage Image Segmentation by Adaptive Thresholding and Gradient Watershed

CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
A Comparison of Affine Region Detectors

International Journal of Computer Vision
Support vector machines for histogram-based image classification

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper proposes an intelligent video surveillance system to estimate the crowd density by effective region feature extracting (ERFE) and learning. Firstly, motion detection method is utilized to segment the foreground, and the extremal regions of the foreground are then extracted. Furthermore, a new perspective projection method is proposed to modify the 3D to 2D distortion of the extracted regions, and the moving cast shadow is eliminated based on the color invariant of the shadow region. Afterwards, histogram statistic method is applied to extract crowd features from the modified regions. Finally, the crowd features are classified into a range of density levels by using support vector machine. Experiments on real crowd videos show that the proposed density estimation system has great advantage in large-scale crowd analysis. And more importantly, better performance is achieved even on variant view angle or illumination changing conditions. Thus the video surveillance system is more robust and practical.