DocumentCode :
598223
Title :
Abnormal crowd behavior detection based on social attribute-aware force model
Author :
Yanhao Zhang ; Lei Qin ; Hongxun Yao ; Qingming Huang
Author_Institution :
Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China
fYear :
2012
fDate :
Sept. 30 2012-Oct. 3 2012
Firstpage :
2689
Lastpage :
2692
Abstract :
In this paper, a novel social attribute-aware force model is presented for abnormal crowd pattern detection in video sequences. We take social characteristics of crowd behaviors into account in order to improve the effectiveness of the simulation on the interaction behaviors of the crowd. A quick unsupervised method is proposed to estimate the scene scale. Both the social disorder attribute and congestion attribute are introduced to describe the realistic social behaviors by using statistical context feature. Through the semantic attribute-aware enhancement, we obtain an improved model on the basis of social force. We validate our method in public available datasets for abnormal detection, and the experimental results show promising performance compared with other state of the art methods.
Keywords :
image sequences; statistical analysis; video signal processing; abnormal crowd pattern detection; congestion attribute; interaction behaviors; quick unsupervised method; realistic social behaviors; scene scale estimation; semantic attribute-aware enhancement; social attribute-aware force model; social disorder attribute; social force; statistical context feature; video sequences; Computational modeling; Computer vision; Dynamics; Force; Hidden Markov models; Image motion analysis; Semantics; Abnormal Detection; Attributes; Crowd Behaviors; Social Force Model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
ISSN :
1522-4880
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2012.6467453
Filename :
6467453
Link To Document :
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