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Signal, Image and Video Processing

, Volume 12, Issue 6, pp 1189–1196 | Cite as

Object tracking based on support vector dictionary learning

  • Li Lv
  • Zhe Chen
  • Zhen Zhang
  • Tanghuai Fan
  • Lizhong Xu
Original Paper
  • 131 Downloads

Abstract

Dictionary learning is widely used to track targets in video sequences. However, a target can be lost during the tracking because of rotation, motion, background clutter, and so on. A dictionary learning method has recently been developed to reduce the chances of missing the target. We developed a new approach using support vector dictionary learning with histograms of sparse codes for a particle filter framework. The representation with support vector can help balance the residual between the candidate and the target. The experiments conducted on challenging sequences demonstrate that the proposed method outperforms seven state-of-the-art algorithms in terms of the overlap rate, center error, and accuracy.

Keywords

Object tracking Support vector dictionary Target representation Particle filter 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61563036, 61501173, and 61461032 and Natural Science Foundation of Jiangxi Province under Grant No. 20161BAB212037.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Li Lv
    • 1
    • 2
  • Zhe Chen
    • 1
  • Zhen Zhang
    • 1
  • Tanghuai Fan
    • 2
  • Lizhong Xu
    • 1
  1. 1.College of Computer and InformationHohai UniversityNanjingChina
  2. 2.School of Information EngineeringNanchang Institute of TechnologyNanchangChina

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