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Blob Tracking with Adaptive Feature Selection and Accurate Scale Determination

  • Jingping Jia
  • David Feng
  • Yanmei Chai
  • Rongchun Zhao
  • Zheru Chi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)

Abstract

We propose a novel color based tracking framework in which an object configuration and color feature are simultaneously determined via scale space filtration. The tracker can automatically select discriminative color feature that well distinguishes foreground from background. According to that feature, a likelihood image of the target is generated for each incoming frame. The target’s area turns into a blob in the likelihood image. The scale of this blob can be determined based on the local maximum of differential scale-space filters. We employ the QP_TR trust region algorithm to search for the local maximum of multi-scale normalized Laplacian filter of the likelihood image to locate the target as well as determine its scale. Based on the tracking results of sequence examples, the proposed method has been proven to be resilient to the color and lighting changes, be capable of describing the target more accurately and achieve much better tracking precision.

Keywords

Trust Region Scale Space Trust Region Method Candidate Feature Initial Trust 
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
    • 2
  • David Feng
    • 1
    • 3
  • Yanmei Chai
    • 2
  • Rongchun Zhao
    • 1
    • 2
  • Zheru Chi
    • 1
    • 3
  1. 1.Department of Electronic and Information EngineeringThe Hong Kong Polytechnic UniversityHong Kong
  2. 2.School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anP.R. China
  3. 3.School of Information Technologies University of SydneyAustralia

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