Machine Vision and Applications

, Volume 23, Issue 3, pp 501–511

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

Original Paper

DOI: 10.1007/s00138-011-0341-0

Cite this article as:
Wang, B., Ye, M., Li, X. et al. Machine Vision and Applications (2012) 23: 501. doi:10.1007/s00138-011-0341-0


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.


Local spatio-temporal cuboid Wavelet transform High-frequency information Latent Dirichlet allocation (LDA) Multiple Hidden Markov Models (HMMs) 

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Bo Wang
    • 1
    • 2
  • Mao Ye
    • 1
    • 2
  • Xue Li
    • 3
  • Fengjuan Zhao
    • 1
    • 2
  • Jian Ding
    • 1
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  3. 3.School of Information Technology and Electronic EngineeringThe University of QueenslandBrisbaneAustralia

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