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Part-based visual tracking with spatially regularized correlation filters

  • Dejun ZhangEmail author
  • Zhao Zhang
  • Lu Zou
  • Zhuyang Xie
  • Fazhi He
  • Yiqi Wu
  • Zhigang Tu
Original Article

Abstract

Discriminative Correlation Filters (DCFs) have demonstrated excellent performance in visual object tracking. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on image patches; unfortunately, this also introduces unwanted boundary effects. Recently, Spatially Regularized Discriminative Correlation Filters (SRDCFs) were proposed to resolve this issue by introducing penalization weights to the filter coefficients, thereby efficiently reducing boundary effects by assigning higher weights to the background. However, due to the variable target scale, defining the penalization ratio is non trivial; thus, it is possible to penalize the image content while also penalizing the background. In this paper, we investigate SRDCFs and present a novel and efficient part-based tracking framework by exploiting multiple SRDCFs. Compared with existing trackers, the proposed method has several advantages. (1) We define multiple correlation filters to extract features within the range of the object, thereby alleviating the boundary effect problem and avoiding penalization of the target content. (2) Through the combination of cyclic object shifts with penalized filters to build part-based object trackers, there is no need to divide training samples into parts. (3) Comprehensive comparisons demonstrate that our approach achieves a performance equivalent to that of the baseline SRDCF tracker on a set of benchmark datasets, namely, OTB2013, OTB2015 and VOT2017. In addition, compared with other state-of-the-art trackers, our approach demonstrates superior performance.

Keywords

Correlation filter tracking Discriminative Correlation Filter Part-based tracking Spatially regularized filter 

Notes

Acknowledgements

This study was funded by the National Natural Science Foundation of China (Grant nos. 61702350 and 61472289) and the Open Project Program of State Key Laboratory of Digital Manufacturing Equipment and Technology at HUST (Grant no. DMETKF2017016).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Information EngineeringChina University of GeosciencesWuhanChina
  2. 2.College of Information and EngineeringSichuan Agricultural UniversityYaanChina
  3. 3.School of Computer Science and TechnologyWuhan UniversityWuhanChina
  4. 4.College of Computer ScienceChina University of GeosciencesWuhanChina
  5. 5.School of Electrical and Electronic EngineeringNanyang Technological University50 Nanyang AvenueSingapore

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