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Visual object tracking with discriminative correlation filtering and hybrid color feature

  • Yang Huang
  • Zhiqiang ZhaoEmail author
  • Bin Wu
  • Zhuolin Mei
  • Zongmin Cui
  • Guangyong Gao
Article
  • 37 Downloads

Abstract

The technology of visual object tracking based on correlation filter has good accuracy and efficiency. However, it is still necessary to be study further on the appearance model of the target, the scale variation of the target and so on. This paper proposes a tracking algorithm based on discriminative correlation filtering and a hybrid color feature. The hybrid color feature is composed of two parts, which are compressed color name features and Histogram of Oriented Gradient features based on opponent color space. These two parts features above are extracted from the target patch, respectively. For the first part, color-name features are extracted from a target patch firstly, and then block-based compressed color-name features are extracted according to these color-name features. For the second part, opponent color features are extracted from the target patch firstly, and then HOG features are extracted from these opponent color features. At the basis of the hybrid color feature, two different discriminative correlation filters are used to estimate the translation and the scale of the target, respectively. Finally, extensive experiments show that the tracking algorithm with the hybrid color features of this paper outperforming several state-of-the-art tracking algorithms.

Keywords

Object tracking Hybrid color feature Correlation filtering Histogram of oriented gradients Opponent Color name 

Notes

Acknowledgment

Thank the editor and the anonymous referees for their valuable comments. This research was supported by the Science and Technology Research Project of Jiangxi Education Department (No. GJJ180904), the National Natural Science Foundation of China (No. 61762055, 61572214 and 61662039), the Jiangxi Provincial Natural Science Foundation of China (No. 20181BAB202014) and the Humanities and Social Sciences Foundation of Colleges and Universities in Jiangxi Province (No. TQ18111).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and TechnologyJiujiang UniversityJiuJiangChina
  2. 2.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

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