trackrecognition_tip13

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IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 2, FEBRUARY 2013549

WhaA!" W" T!a#$%&'( A U&%)%"* A++!a#h )T!a#$%&' a&* R"#'&%%&

-%a/" Fa&, %ah/% Sh"&, Student Member, IEEE, a&* Y%&' W/, Senior Member, IEEE

Abstract Tracking is essentially a matchingproblem. While traditional tracking methods

mostly focus on low-level imagecorrespondences between frames, we argue thathigh-level seman- tic correspondences areindispensable to make tracking more reliable.Based on that, a unified approach of low-levelobject tracking and high-level recognition isproposed for single object tracking, in which thetarget category is actively recognied duringtracking. !igh-level offline modelscorresponding to the recognied category arethen adaptively selected and combined with low-level online tracking models so as to achievebetter tracking performance. "#tensivee#perimental results show that our approachoutperforms state-of-the-art online models inmany challenging tracking scenarios such asdrastic view change, scale change, backgroundclutter, and morphable objects.

Index Terms $bject recognition, videoanalysis, visual tracking.

I. INTROUCTION

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III.FORMULATION

A. OverviewDescription

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TABLE I

AVERAGE CENTER LOCAT ION ERRORS IN PIELS :. SO( KSIMULTANEOUSOPTIMIHATION , AI( KAUTOMAT IC INITIALI HATION

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TABLE II

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trackrecognition_tip13

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