High-Speed Tracking with Multi-kernel Correlation Filters
Open accessAbstract
Correlation filter (CF) based trackers are currently ranked top in terms of their performances. Nevertheless, only some of them, such as KCF [26] and MKCF [48], are able to exploit the powerful discriminability of non-linear kernels. Although MKCF achieves more powerful discriminability than KCF through introducing multi-kernel learning (MKL) into KCF, its improvement over KCF is quite limited and its computational burden increases significantly in comparison with KCF. In this paper, we will introduce the MKL into KCF in a different way than MKCF. We reformulate the MKL version of CF objective function with its upper bound, alleviating the negative mutual interference of different kernels significantly. Our novel MKCF tracker, MKCFup, outperforms KCF and MKCF with large margins and can still work at very high fps. Extensive experiments on public data sets show that our method is superior to state-of-the-art algorithms for target objects of small move at very high speed.
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Discriminative methods are superior in terms of accuracy and computational cost.
“As per the literature, discriminative methods are superior in terms of accuracy and computational cost. Some of the examples of these trackers are given in [1,10-12].”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [12]
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References 60 · Cited by 103
- Authors
- Tang, M., Yu, B., Zhang, F., & Wang, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Beijing University of Posts and Telecommunications (CN)
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 4874-4883
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964264751
- Cited by
- 103
- References
- 60
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 9 · 2023: 9 · 2022: 9 · 2021: 17 · 2020: 29 · 2019: 18 · 2018: 2
- FWCI
- 4.97
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- correlation filter tracking, multiple kernel learning, object tracking, kernelized correlation filter, high-speed tracking, small object tracking
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