Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking
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LCT achieved a mean overlap success rate of 0.717.
“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]
The Introduction (p. 2) describes the authors’ tracker, supporting the citation’s identification of LCT. The overlap-success results in Overall Performance (pp. 12–13) do not report a mean rate of 0.717 for it, so the numerical part of the claim is not supported by our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredIn the suitcase_ce sequence, MKCF shows similar performance to the proposed algorithm, whereas MACF, STRCF, and LCT were unable to track the correct object.
“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]
LCT achieved a mean center location error of 70.797 pixels.
“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]
Section 6.8 (p. 18) says the paper compares trackers’ center location errors, and the Figure 19 caption (p. 19) describes those results. Neither passage gives a mean error of 70.797 pixels for LCT. Searches for the exact value, LCT, and mean center location error found no passage verifying the claimed number. (LLM, conf=0.74)
Experimental — accuracy not yet measuredLCT lost the target in frame 500 of the car1 sequence.
“LCT [21] lost the target in frame number 500”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [21]
The Qualitative Evaluation (pp. 16–18) discusses tracking failures on other sequences, but does not report LCT losing the target on car1 at frame 500. Searches for “car1,” “LCT,” and “frame 500” found no passage establishing that event in our copy. (LLM, conf=0.74)
Experimental — accuracy not yet measuredIn the microbike_ce sequence, LCT fails at the start of the video sequence (frame 50).
“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]
The qualitative evaluations (pp. 16, 18–19) discuss failures on other sequences but do not address microbike_ce or frame 50. Searches for “microbike_ce” and “LCT” found no passage documenting the claimed failure. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- 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 · 2017: 1
- 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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