Visual tracking using discriminative representation with ℓ2 regularization

Research Article

Abstract

In this paper, we propose a novel visual tracking method using a discriminative representation under a Bayesian framework. First, we exploit the histogram of gradient (HOG) to generate the texture features of the target templates and candidates. Second, we introduce a novel discriminative representation and ℓ2-regularized least squares method to solve the proposed representation model. The proposed model has a closed-form solution and very high computational efficiency. Third, a novel likelihood function and an update scheme considering the occlusion factor are adopted to improve the tracking performance of our proposed method. Both qualitative and quantitative evaluations on 15 challenging video sequences demonstrate that our method can achieve more robust tracking results in terms of the overlap rate and center location error.

Keywords

visual tracking discriminative representation Bayesian framework closed-form solution 

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Notes

Acknowledgements

This work was supported by a project of Shandong Province Higher Educational Science and Technology Program (J17KA088 and J16LN02), the Natural Science Foundation of Shandong Province (ZR2015FL009 and ZR2014FL020), the Key Research and Development Program of Shandong Province (2016GGX101023), and the Science Foundation of Binzhou University (BZXYG1524).

Supplementary material

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Civil AviationNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Aviation Information Technology Research and Development CenterBinzhou UniversityBinzhouChina

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