Title

Abnormal Crowd Behavior Detection Using Social Force Model

Abstract

In this paper we introduce a novel method to detect and localize abnormal behaviors in crowd videos using Social Force model. For this purpose, a grid of particles is placed over the image and it is advected with the space-time average of optical flow. By treating the moving particles as individuals, their interaction forces are estimated using social force model. The interaction force is then mapped into the image plane to obtain Force Flow for every pixel in every frame. Randomly selected spatio-temporal volumes of Force Flow are used to model the normal behavior of the crowd. We classify frames as normal and abnormal by using a bag of words approach. The regions of anomalies in the abnormal frames are localized using interaction forces. The experiments are conducted on a publicly available dataset from University of Minnesota for escape panic scenarios and a challenging dataset of crowd videos taken from the web. The experiments show that the proposed method captures the dynamics of the crowd behavior successfully. In addition, we have shown that the social force approach outperforms similar approaches based on pure optical flow. ©2009 IEEE.

Publication Date

1-1-2009

Publication Title

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Number of Pages

935-942

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPRW.2009.5206641

Socpus ID

70450255364 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/70450255364

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