Video Segmentation Framework Based on Multi-kernel Representations and Feature Relevance Analysis for Object Classification

  • S. Molina-Giraldo
  • J. Carvajal-González
  • A. M. Álvarez-Meza
  • G. Castellanos-Domínguez
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 318)

Abstract

A video segmentation framework to automatically detect moving objects in a scene using static cameras is proposed. Using Multiple Kernel Representations, we aim to enhance the data separability into the scene by incorporating multiple information sources into the process, and employing a relevance analysis each source is automatically weighted. A tuned Kmeans technique is employed to group pixels as static or moving objects. Moreover, the proposed methodology is tested for the classification of people and abandoned objects. Attained results over real-world datasets, show how our approach is stable using the same parameters for all experiments.

Keywords

Background subtraction Multiple kernel learning Relevance analysis Data separability 

Notes

Acknowledgments

This research was carried out under grants provided by a M.Sc. and a Ph.D. scholarship provided by Universidad Nacional de Colombia, and the project 15,795, funded by Universidad Nacional de Colombia.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • S. Molina-Giraldo
    • 1
  • J. Carvajal-González
    • 1
  • A. M. Álvarez-Meza
    • 1
  • G. Castellanos-Domínguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia

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