PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Topic Models for Scene Analysis and Abnormality Detection
Jagan Varadarajan and Jean-Marc Odobez
In: Proc. ICCV Visual Surveillance workshop (ICCV-VS, Kyoto(2009).

Abstract

Automatic analysis and understanding of common activities and detection of deviant behaviors is a challenging task in computer vision. This is particularly true in surveillance data, where busy traffic scenes are rich with multifarious activities many of them occurring simultaneously. In this paper, we address these issues with an unsupervised learning approach relying on probabilistic Latent Semantic Analysis (pLSA) applied to a rich set visual features including motion and size activities for discovering relevant activity patterns occurring in such scenes. We then show how the discovered patterns can directly be used to segment the scene into regions with clear semantic activity content. Furthermore, we introduce novel abnormality detection measures within the scope of the adopted modeling approach, and investigate in detail their performance with respect to various issues. Experiments on 45 minutes of video captured from a busy traffic scene and involving abnormal events are conducted.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:6772
Deposited By:Jean-Marc Odobez
Deposited On:08 March 2010