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Adaptive Object Tracking with Online Statistical Model Update

  • KaiYeuh Chang
  • Shang-Hong Lai
Conference paper
  • 1.5k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3852)

Abstract

In this paper, we propose a statistical model-based contour tracking algorithm based on the Condensation framework. The models include a novel object shape prediction model and two statistical object models. The object models consist of the grayscale histogram and contour shape PCA models computed from the previous tracking results. With the incremental singular value decomposition (SVD) technique, these three models are learned and updated very efficiently during tracking. We show that the proposed shape prediction model outperforms the affine predictor through experiments. Experimental results show that the proposed contour tracking algorithm is very stable in tracking human heads on real videos with object scaling, rotation, partial occlusion, and illumination changes.

Keywords

Singular Value Decomposition Particle Filter Object Tracking Visual Tracking Image Contour 
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

  • KaiYeuh Chang
    • 1
  • Shang-Hong Lai
    • 1
  1. 1.Dept. of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan

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