Robust visual tracking via a hybrid correlation filter

In this paper, we propose a hybrid correlation filter based tracking method which depends on coupled interactions between a global filter and two local filters. Specifically, a local kernel feature with Gaussian curvature is developed to encode object appearance. Then the global filter and the two l...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 78; no. 22; pp. 31633 - 31648
Main Authors Wang, Yong, Luo, Xinbin, Ding, Lu, Wu, Jingjing, Fu, Shan
Format Journal Article
LanguageEnglish
Published New York Springer US 01.11.2019
Springer Nature B.V
Subjects
Accuracy
Algorithms
Computer Communication Networks
Computer engineering
Computer Science
Curvature
Data Structures and Information Theory
Electrical engineering
Methods
Multimedia
Multimedia Information Systems
Occlusion
Optical tracking
Random variables
Sidelobes
Special Purpose and Application-Based Systems
Global filter
Correlation filter based tracking
Peak-to-sidelobe ratio
Gaussian curvature
Local filter
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Summary:In this paper, we propose a hybrid correlation filter based tracking method which depends on coupled interactions between a global filter and two local filters. Specifically, a local kernel feature with Gaussian curvature is developed to encode object appearance. Then the global filter and the two local filters independently track the target. The peak-to-sidelobe ratio (PSR) is employed to measure the reliability of the tracking results. Next, the global filter and the two local filters jointly determine the target position. In this way, the proposed hybrid model deals well with challenging situations, e.g., partial occlusion and scale changes. Experiments on large benchmark datasets show that our method performs favorably against state-of-the-art trackers.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-07851-3