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QP_TR Trust Region Blob Tracking Through Scale-Space with Automatic Selection of Features

  • Jingping Jia
  • Qing Wang
  • Yanmei Chai
  • Rongchun Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4141)

Abstract

A new approach of tracking objects in image sequences is proposed, in which the constant changes of the size and orientation of the target can be precisely described. For each incoming frame, a likelihood image of the target is created according to the automatically chosen best feature, where the target’s area turns into a blob. The scale of this blob can be determined based on the local maxima of differential scale-space filters. We employ the QP_TR trust region algorithm to search for the local maxima of orientational multi-scale normalized Laplacian filter of the likelihood image to locate the target as well as to determine its scale and orientation. Based on the tracking results of sequence examples, the novel method has been proven to be capable of describing the target more accurately and thus achieves much better tracking precision.

Keywords

Trust Region Trust Region Method Candidate Feature Trust Region Algorithm Likelihood Image 
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

  • Jingping Jia
    • 1
  • Qing Wang
    • 1
  • Yanmei Chai
    • 1
  • Rongchun Zhao
    • 1
  1. 1.School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anP.R. China

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