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Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking
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“while the remaining three trackers, i.e., STRCF [28], LCT [21], and MKCF [5], have a mean overlap success rate of 0.799, 0.717, and 0.645, respectively.”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [21]
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In the citing paper
“MKCF [5] showed a similar performance to our proposed algorithm on this sequence, whereas MACF [23], STRCF [28], and LCT [21] were unable to track the correct object.”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [21]
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In the citing paper
“LCT [21] and MKCF [5] showed similar performance in terms of center location error by achieving 70.797 and 79.782 values, respectively.”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [21]
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“LCT [21] lost the target in frame number 500”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [21]
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“In this sequence, LCT [21] fails at the start of the video sequence, which can be seen in frame number 50.”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [21]
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Citations
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Metadata
- Authors
- Ma, C., Huang, J.-B., Yang, X., & Yang, M.-H.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Virginia Tech (US)
- University of California, Merced (US)
- Corresponding author
- Yang, Ming-Hsuan
- Venue
- International Journal of Computer Vision
- Published
- 2018-03-16
- Volume
- 126
- Issue
- 8
- Pages
- 771-796
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2735270199
- Cited by
- 156
- References
- 62
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 7 · 2023: 14 · 2022: 21 · 2021: 26 · 2020: 35 · 2019: 32 · 2018: 10
- FWCI
- 12.88
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Journal of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Division of Computing and Communication Foundations (1149783)
- 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
- object tracking, long-term memory, short-term memory, kernelized correlation filter, feature pyramid, tracking failure recovery, occlusion, sliding window detection, learning rate
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