research-article

Improved kernelized correlation filter tracking by using spatial regularization

Authors:

Jifeng NingAuthors Info & Claims

Volume 50, Issue C

Pages 74 - 82

https://doi.org/10.1016/j.jvcir.2017.11.008

Published: 01 January 2018 Publication History

Highlights

•

We propose an efficient way to solve the spatial regularized regression formula.

•

We propose a real-time tracking algorithm base on the correlation filter.

•

The new algorithm achieves comparable performance and higher speed with SRDCF.

Abstract

The correlation filter based trackers have drawn much attention due to their encouraging performance on precision, robustness and speed. In this paper, we introduce the spatial regularization component into the ridge regression model used by classical kernelized correlation filter (KCF) to improve its performance. It overcomes the fact that the traditional KCF does not consider the prior spatial constraint of the feature distribution of the target. We found that, after adding the spatial regularized function, we can solve the ridge regression formula efficiently with the property of circulant matrices. In this way, we can simultaneously keep the realtime and improve the tracking performance. Finally, we evaluate the proposed SRKCF tracker on the OTB-2013 and OTB-2015 comparing with 36 trackers and our tracker achieves state-of-art. Comparing with the SRDCF which applies the spatial regularized function, our algorithm achieves comparable performance with the obvious advantages in speed.

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Cited By

Chen LGao LJiang YLi YHe GNing J(2024)Local-Global Self-Attention for Transformer-Based Object TrackingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.343494934:12_Part_1(12316-12329)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TCSVT.2024.3434949

Index Terms

Improved kernelized correlation filter tracking by using spatial regularization
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding
  2. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Canonical correlation analysis
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Journal of Visual Communication and Image Representation

Journal of Visual Communication and Image Representation Volume 50, Issue C

Jan 2018

333 pages

ISSN:1047-3203

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Elsevier Inc.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 January 2018

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Citations

Cited By

Chen LGao LJiang YLi YHe GNing J(2024)Local-Global Self-Attention for Transformer-Based Object TrackingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.343494934:12_Part_1(12316-12329)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TCSVT.2024.3434949

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