Computer Vision – ACCV 2009

Volume 5994 of the series Lecture Notes in Computer Science pp 201-212

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

  • Tatsuya TanakaAffiliated withKyushu University
  • , Atsushi ShimadaAffiliated withKyushu University
  • , Rin-ichiro TaniguchiAffiliated withKyushu University
  • , Takayoshi YamashitaAffiliated withOMRON Corp. Kyoto
  • , Daisaku AritaAffiliated withKyushu UniversityInstitute of Systems, Information Technologies and Nanotechnologies (ISIT)

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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.