Multiple features fusion for crowd density estimation

Authors:
Zi Ye;Jinqiao Wang;Zhenchong Wang;Hanqing Lu
Affiliations:
China University of Mining and Technology (Beijing);Chinese Academy of Sciences, Beijing, China;China University of Mining and Technology (Beijing);Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Year:
2012

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Abstract

Crowd density estimation, is much valuable in intelligent crowd monitoring. The traditional approach based on static texture analysis of single frame, is not adept to complex background, and the rule based statistic approaches are short of robustness for background noise. In this paper, a crowd density estimation approach fusing statistic features and texture analysis was proposed. After extracting foreground objects with frame difference, we learn SVM classifiers with GLCM and statistical features. The experiment results show the superiority of the proposed method and it can be applied in a complex environment.