Abnormal crowd behavior detection using size-adapted spatio-temporal features

Wang, Bo, Ye, Mao, Li, Xue and Zhao, Fengjuan (2011) Abnormal crowd behavior detection using size-adapted spatio-temporal features. International Journal of Control Automation and Systems, 9 5: 905-912. doi:10.1007/s12555-011-0511-x

Author Wang, Bo
Ye, Mao
Li, Xue
Zhao, Fengjuan
Title Abnormal crowd behavior detection using size-adapted spatio-temporal features
Journal name International Journal of Control Automation and Systems   Check publisher's open access policy
ISSN 1598-6446
Publication date 2011-10
Sub-type Article (original research)
DOI 10.1007/s12555-011-0511-x
Volume 9
Issue 5
Start page 905
End page 912
Total pages 8
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. However, complex real-life situations (e.g., severe occlusion, over-crowding, etc.) still challenge the effectiveness of previous algorithms. Recently, the methods based on spatio-temporal cuboid are popular in video analysis. To our knowledge, the spatio-temporal cuboid is always extracted randomly from a video sequence in the existing methods. The size of each cuboid and the total number of cuboids are determined empirically. The extracted features either contain the redundant information or lose a lot of important information which extremely affect the accuracy. In this paper, we propose an improved method. In our method, the spatio-temporal cuboid is no longer determined arbitrarily, but by the information contained in the video sequence. The spatio-temporal cuboid is extracted from video sequence with adaptive size. The total number of cuboids and the extracting positions can be determined automatically. Moreover, to compute the similarity between two spatio-temporal cuboids with different sizes, we design a novel data structure of codebook which is constructed as a set of two-level trees. The experiment results show that the detection rates of false positive and false negative are significantly reduced. Keywords: Codebook, latent dirichlet allocation (LDA), social force model, spatio-temporal cuboid.
Keyword Codebook
Latent dirichlet allocation (LDA)
Social force model
Spatio-temporal cuboid
Social force model
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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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Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
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