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Tracking Completion

  • Yao Sui
  • Guanghui Wang
  • Yafei Tang
  • Li Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9912)

Abstract

A fundamental component of modern trackers is an online learned tracking model, which is typically modeled either globally or locally. The two kinds of models perform differently in terms of effectiveness and robustness under different challenging situations. This work exploits the advantages of both models. A subspace model, from a global perspective, is learned from previously obtained targets via rank-minimization to address the tracking, and a pixel-level local observation is leveraged simultaneously, from a local point of view, to augment the subspace model. A matrix completion method is employed to integrate the two models. Unlike previous tracking methods, which locate the target among all fully observed target candidates, the proposed approach first estimates an expected target via the matrix completion through partially observed target candidates, and then, identifies the target according to the estimation accuracy with respect to the target candidates. Specifically, the tracking is formulated as a problem of target appearance estimation. Extensive experiments on various challenging video sequences verify the effectiveness of the proposed approach and demonstrate that the proposed tracker outperforms other popular state-of-the-art trackers.

Keywords

Matrix completion Object tracking Subspace model Local observation Appearance estimation 

Notes

Acknowledgments

The work is partly supported by the National Natural Science Foundation of China (NSFC) under grants 61273282, 61573351 and 61132007, and the joint fund of Civil Aviation Research by the National Natural Science Foundation of China (NSFC) and Civil Aviation Administration under grant U1533132.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of EECSUniversity of KansasLawrenceUSA
  2. 2.China Unicom Research InstituteBeijingChina
  3. 3.Department of EETsinghua UniversityBeijingChina
  4. 4.National Laboratory of Pattern RecognitionInstitute of Automation, CASBeijingChina

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