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Learning from Mistakes: Object Movement Classification by the Boosted Features

  • Shigeyuki Odashima
  • Tomomasa Sato
  • Taketoshi Mori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

This paper proposes a robust object movement detection method via a classifier trained by mis-detection samples. The mis-detection are related to the environment, such as reflection on a display or small movement of a curtain, so learning the patterns of mis-detections will improve the detection precision. The mis-detections are expected to have several features, but selecting manually optimal features and thresholds is difficult. In order to acquire optimal classifier automatically, we employ a ensemble learning framework. The experiment shows the method can detect object movements sufficiently by constructing the classifier automatically by the proposed framework.

Keywords

Object Movement Color Histogram Stable Change Object Candidate Object Movement Detection 
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 2011

Authors and Affiliations

  • Shigeyuki Odashima
    • 1
  • Tomomasa Sato
    • 1
  • Taketoshi Mori
    • 1
  1. 1.Graduate School of Information Science and TechnologyThe University of TokyoTokyoJapan

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