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Skin-Color Based Human Tracking Using a Probabilistic Noise Model Combined with Neural Network

  • Jin Young Kim
  • Min-Gyu Song
  • Seung You Na
  • Seong-Joon Baek
  • Seung Ho Choi
  • Joohun Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

We develop a simple and fast human tracking system based on skin-color using Kalman filter for humanoid robots. For our human tracking system we propose a fuzzy and probabilistic model of observation noise, which is important in Kalman filter implementation. The uncertainty of the observed candidate region is estimated by neural network. Neural network is also used for the verification of face-like regions obtained from skin-color information. Then the probability of observation noise is controlled based on the uncertainty value of the observation. Through the real-human tracking experiments we compare the performance of the proposed model with the conventional Gaussian noise model. The experimental results show that the proposed model enhances the tracking performance and also can compensate the biased estimations of the baseline system.

Keywords

Kalman Filter Noise Model Candidate Region Humanoid Robot Face 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 2006

Authors and Affiliations

  • Jin Young Kim
    • 1
  • Min-Gyu Song
    • 1
  • Seung You Na
    • 1
  • Seong-Joon Baek
    • 1
  • Seung Ho Choi
    • 2
  • Joohun Lee
    • 3
  1. 1.Dept. of Electronics Eng.Chonnam National UniversityGwangjooSouth Korea
  2. 2.Dept. of Multimedia Eng.Dongshin UniversityChollanamdoSouth Korea
  3. 3.Dept. of Internet Broadcasting Dong-A Broadcasting CollegeAnsungSouth Korea

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