Feature Selection by Relevance Analysis for Abandoned Object Classification

  • Johanna Carvajal-González
  • AndrésM. Álvarez-Meza
  • German Castellanos-Domínguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

A methodology to classify abandoned objects in video surveillance environments is proposed. Our aim is to determine a set of relevant features that properly describes the main patterns of the objects. Assuming that the abandoned object was previously detected by a visual surveillance framework, a preprocessing stage to segment the region of interest from a given detected object is also presented. Then, some geometric and Hu’s moments features are estimated. Moreover, a relevance analysis is employed to identify which features reveal the major variability of the input space to discriminate among different objects. Attained results over a real-world video surveillance dataset show how our approach is able to select a subset of features for achieving stable classification performance. Our approach seems to be a good alternative to support the development of automated video surveillance systems.

Keywords

Video surveillance abandoned object classification feature relevance analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Johanna Carvajal-González
    • 1
  • AndrésM. Álvarez-Meza
    • 1
  • German Castellanos-Domínguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia

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