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Unsupervised Learning of Functional Categories in Video Scenes

  • Matthew W. Turek
  • Anthony Hoogs
  • Roderic Collins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)

Abstract

Existing methods for video scene analysis are primarily concerned with learning motion patterns or models for anomaly detection. We present a novel form of video scene analysis where scene element categories such as roads, parking areas, sidewalks and entrances, can be segmented and categorized based on the behaviors of moving objects in and around them. We view the problem from the perspective of categorical object recognition, and present an approach for unsupervised learning of functional scene element categories. Our approach identifies functional regions with similar behaviors in the same scene and/or across scenes, by clustering histograms based on a trajectory-level, behavioral codebook. Experiments are conducted on two outdoor webcam video scenes with low frame rates and poor quality. Unsupervised classification results are presented for each scene independently, and also jointly where models learned on one scene are applied to the other.

Keywords

Feature Vector Functional Category Anomaly Detection Unsupervised Learn Semantic Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Matthew W. Turek
    • 1
  • Anthony Hoogs
    • 1
  • Roderic Collins
    • 1
  1. 1.Kitware, Inc.Clifton ParkU.S.A.

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