A Robust Kernelized Correlation Tracking Algorithm for Infrared Targets Based on Ensemble Learning

XIE Tao; WU Ensi

doi:10.11999/JEIT170527

Volume 40 Issue 3

Mar. 2018

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XIE Tao, WU Ensi. A Robust Kernelized Correlation Tracking Algorithm for Infrared Targets Based on Ensemble Learning[J]. Journal of Electronics & Information Technology, 2018, 40(3): 602-609. doi: 10.11999/JEIT170527

Citation:

XIE Tao, WU Ensi. A Robust Kernelized Correlation Tracking Algorithm for Infrared Targets Based on Ensemble Learning[J]. Journal of Electronics & Information Technology, 2018, 40(3): 602-609. doi: 10.11999/JEIT170527

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A Robust Kernelized Correlation Tracking Algorithm for Infrared Targets Based on Ensemble Learning

doi: 10.11999/JEIT170527

XIE Tao¹,
WU Ensi²

1.
(College of Computer and Information Science, Southwest University, Chongqing 400715, China)
2.
(Office of Educational Administration, Chongqing Normal University, Chongqing 401331, China)

Funds:

The Ministry of Education-China Mobile Research Fund Project (MCM20160405)

Received Date: 2017-05-31
Rev Recd Date: 2017-12-05
Publish Date: 2018-03-19

Abstract

Abstract

In the infrared object tracking, the single classifier is not enough to fit the multimodal data due to the complex background information of the target and the significant change in the appearance. In this paper, Kernelized Correlation Filters (KCF) tracking algorithm is used to integrate kernelized correlation classifiers into one framework through ensemble learning. It uses the KCF classifier that has analytical solutions to balance the contradiction between the robustness and instantaneity, thereby addressing the complex background and significant appearance changes, and consequently significantly improving the tracking performance and stability. To verify the effectiveness of the algorithm, this paper uses two kernelized correlation trackers to learn a strong classifier. The qualitative and quantitative experiments show that the proposed algorithm outperforms the traditional KCF algorithm, and the tracking speed is superior to most of the comparison algorithms.
- Object tracking,
- Ensemble learning,
- Discriminant classifier,
- Kernelized correlation tracking

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References(27)

References

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