Towards Robust Object Detection: Integrated Background Modeling Based on Spatio-temporal Features

  • Tatsuya Tanaka
  • Atsushi Shimada
  • Rin-ichiro Taniguchi
  • Takayoshi Yamashita
  • Daisaku Arita
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5994)

Abstract

We propose a sophisticated method for background modeling based on spatio-temporal features. It consists of three complementary approaches: pixel-level background modeling, region-level one and frame-level one. The pixel-level background model uses the probability density function to approximate background model. The PDF is estimated non-parametrically by using Parzen density estimation. The region-level model is based on the evaluation of the local texture around each pixel while reducing the effects of variations in lighting. The frame-level model detects sudden, global changes of the the image brightness and estimates a present background image from input image referring to a background model image. Then, objects are extracted by background subtraction. Fusing their approaches realizes robust object detection under varying illumination, which is shown in several experiments.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tatsuya Tanaka
    • 1
  • Atsushi Shimada
    • 1
  • Rin-ichiro Taniguchi
    • 1
  • Takayoshi Yamashita
    • 2
  • Daisaku Arita
    • 1
    • 3
  1. 1.Kyushu UniversityFukuokaJapan
  2. 2.OMRON Corp. KyotoJapan
  3. 3.Institute of Systems, Information Technologies and Nanotechnologies (ISIT)FukuokaJapan

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