Abnormal crowd behavior detection using high-frequency and spatio-temporal features

Wang, Bo, Ye, Mao, Li, Xue, Zhao, Fengjuan and Ding, Jian (2012) Abnormal crowd behavior detection using high-frequency and spatio-temporal features. Machine Vision and Applications, 23 3: 501-511.


Author Wang, Bo
Ye, Mao
Li, Xue
Zhao, Fengjuan
Ding, Jian
Title Abnormal crowd behavior detection using high-frequency and spatio-temporal features
Journal name Machine Vision and Applications   Check publisher's open access policy
ISSN 0932-8092
1432-1769
Publication date 2012-05
Year available 2011
Sub-type Article (original research)
DOI 10.1007/s00138-011-0341-0
Volume 23
Issue 3
Start page 501
End page 511
Total pages 11
Place of publication Heidelberg, Germany
Publisher Springer
Collection year 2012
Language eng
Abstract Abnormal crowd behavior detection is an important research issue in computer vision. The traditional methods first extract the local spatio-temporal cuboid from video. Then the cuboid is described by optical flow or gradient features, etc. Unfortunately, because of the complex environmental conditions, such as severe occlusion, over-crowding, etc., the existing algorithms cannot be efficiently applied. In this paper, we derive the high-frequency and spatio-temporal (HFST) features to detect the abnormal crowd behaviors in videos. They are obtained by applying the wavelet transform to the plane in the cuboid which is parallel to the time direction. The high-frequency information characterize the dynamic properties of the cuboid. The HFST features are applied to the both global and local abnormal crowd behavior detection. For the global abnormal crowd behavior detection, Latent Dirichlet allocation is used to model the normal scenes. For the local abnormal crowd behavior detection, Multiple Hidden Markov Models, with an competitive mechanism, is employed to model the normal scenes. The comprehensive experiment results show that the speed of detection has been greatly improved using our approach. Moreover, a good accuracy has been achieved considering the false positive and false negative detection rates.
Keyword Local spatio-temporal cuboid
Wavelet transform
High-frequency information
Latent Dirichlet Allocation (LDA)
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 15 May 2011

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
 
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