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Adaptive Kernel Based Tracking Using Mean-Shift

  • Jie-Xin Pu
  • Ning-Song Peng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)

Abstract

The mean shift algorithm is an kernel based way for efficient object tracking. However, there is presently no clean mechanism for selecting kernel bandwidth when the object size is changing. We present an adaptive kernel bandwidth selection method for rigid object tracking. The kernel bandwidth is updated by using the object affine model that is estimated by using object corner correspondences between two consecutive frames. The centroid of object is registered by a special backward tracking method. M-estimate method is used to reject mismatched pairs (outliers) so as to get better regression results. We have applied the proposed method to track vehicles changing in size with encouraging results.

Keywords

Object Tracking Consecutive Frame Shift Algorithm Affine Model Kernel Bandwidth 
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

  • Jie-Xin Pu
    • 1
  • Ning-Song Peng
    • 1
  1. 1.Electronic Information Engineering CollegeHenan University of Science and TechnologyLuoyangChina

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