The DOI matches its Crossref record; 61 of the 62 references it cites were found in a registry or an index.
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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
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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 the citing paper
“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
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
(1981) · Cited by 11,963
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Arulampalam, M. S., Maskell, S. R., Gordon, N., & Clapp, T. C. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/78.978374 · Cited by 11,531
High-Speed Tracking with Kernelized Correlation Filters
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753
Cited by
Sorted by citations
Deep Learning for Visual Tracking: A Comprehensive Survey
Marvasti-Zadeh, S. M., Cheng, L., Ghanei-Yakhdan, H., & Kasaei, S. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3046478 · Cited by 434
Deep Regression Tracking with Shrinkage Loss
Lu, X., Ma, C., Ni, B., Yang, X., Reid, I. D., & Yang, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_22 · Cited by 251
Self-citationMulti-UAV Formation Control Based on a Novel Back-Stepping Approach
Zhang, J., Yan, J., & Zhang, P. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.2964847 · Cited by 227
Robust Visual Tracking via Hierarchical Convolutional Features
Ma, C., Huang, J., Yang, X., & Yang, M. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2865311 · Cited by 212
Self-citationVisual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook
Javed, S., Danelljan, M., Khan, F. S., Khan, M. H., Felsberg, M., & Matas, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3212594 · Cited by 201
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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