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Multimedia Tools and Applications

, Volume 77, Issue 23, pp 31447–31467 | Cite as

Visual tracking via robust multi-task multi-feature joint sparse representation

  • Yong Wang
  • Xinbin Luo
  • Lu Ding
  • Shiqiang Hu
Article
  • 69 Downloads

Abstract

Multiple feature object representation has been proved as a robust approach for visual tracking. Different types of situations such as occlusion, rotation and illumination may occur during tracking, especially long sequences. Robust tracking could be obtained as multiple features could complement each other. In this paper, we cast visual tracking as a novel multi-task sparse learning problem and exploit various types of visual features, such as intensity, color, texture and edge, where each feature can be sparsely represented by a linear combination of atoms from an adaptive feature template. We use an on-line feature selection mechanism based on the two-class variance ratio measure, applied to log likelihood distributions computed with respect to a given feature from samples of object and background pixels. The proposed method is integrated in a particle filtering framework. We jointly consider the underlying relationship across different particles, and tackle it in a unified robust multi-task formulation. In addition, to capture the frequently emerging outlier tasks, we make fully use of a decomposition model which enables a more robust and accurate approximation. We show that the proposed model can be efficiently solved using the Alternating direction method of multipliers (ADMM) with a small number of closed-form updates. Four types of features are implemented and tested on numerous benchmark video sequences. Both the qualitative and quantitative results demonstrate the superior performance of the proposed approach compared to 9 state of-the-art trackers.

Keywords

Feature selection Multi-task sparse learning Decomposition model Alternating direction method of multipliers 

Notes

Acknowledgments

This paper is jointly supported by the National Natural Science Foundation of China No. 61374161, China Aviation Science Foundation 20142057006. Part of the research conducted when the first author were in CRCV at UCF.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.School of Electronic information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  3. 3.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina

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