Unusual Event Detection in Crowded Scenes Using Bag of LBPs in spatio-temporal patches
Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2011) Unusual Event Detection in Crowded Scenes Using Bag of LBPs in spatio-temporal patches. In DICTA 2011, IEEE, Noosa, QLD, Australia. (In Press)
Modelling events in densely crowded environments remains challenging, due to the diversity of events and the noise in the scene. We propose a novel approach for anomalous event detection in crowded scenes using dynamic textures described by the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) descriptor. The scene is divided into spatio-temporal patches where LBP-TOP based dynamic textures are extracted. We apply hierarchical Bayesian models to detect the patches containing unusual events. Our method is an unsupervised approach, and it does not rely on object tracking or background subtraction. We show that our approach outperforms existing state of the art algorithms for anomalous event detection in UCSD dataset.
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|Item Type:||Conference Paper|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Institutes > Information Security Institute
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2011 IEEE.|
|Deposited On:||04 Oct 2011 11:52|
|Last Modified:||04 Oct 2011 13:39|
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