The DOI matches its Crossref record; 97 of the 118 references it cites were found in a registry or an index; none of the 11 sources its claims cite is checked for support yet.
Object Tracking Benchmark
Abstract
Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This paper presents a large-scale benchmark for online single-object tracking, with 50 annotated sequences, a code library of 29 trackers, and one-pass, temporal, and spatial robustness evaluations. It uses precision and success plots plus sequence attributes to identify effective tracking components and future research directions.
Key findings
- The benchmark includes 50 fully annotated sequences, 11 attributes, 29 tracking algorithms, and more than 660,000 bounding box outputs; the OCC subset contains 29 sequences.
- In success plots, top-ranked SCM in OPE outperforms Struck by 2.6%, but SCM is 1.9% below Struck in SRE; OPE is described as not the best performance indicator because it is one trial of SRE or TRE.
- Sparse-representation trackers perform well in SRE and TRE; local sparse representations (SCM, ASLA, LSK) outperform holistic sparse templates (MTT, L1APG). On OCC, Struck, SCM, TLD, LSK, and ASLA outperform others; on SV, ASLA, SCM, and Struck perform best; for fast motion, dense-sampling trackers such as Struck, TLD, and CXT perform much better than others.
- Performance often decreases significantly when initialization scale is large (e.g., ×1.2); Struck is less sensitive to scale variation than other well-performing methods, and CSK has the highest speed among the top 10 due to its circulant structure.
Methods
- Built a tracking dataset of 50 fully annotated sequences with 11 attributes and a code library integrating 29 publicly available trackers with uniform input/output formats, plus VIVID testbed methods.
- Used precision plots based on center location error, with representative score at threshold = 20 pixels, and success plots based on bounding box overlap, ranked by area under curve (AUC).
- Evaluated with one-pass evaluation (OPE), temporal robustness evaluation (TRE; each sequence partitioned into 20 segments), and spatial robustness evaluation (SRE; 12 runs per sequence using 8 spatial shifts and 4 scale variations, with shift amount 10% of target size and scale ratios 0.8, 0.9, 1.1, 1.2). OPE tested each tracker on more than 29,000 frames, SRE generated more than 350,000 bounding box results, and TRE performed on around 310,000 frames.
Limitations
- Stated: due to space limitations, the main paper presents only the top 10 trackers and only the OCC, SV, and FM attribute subsets; complete plots are in the supplementary material.
- Stated/evident: evaluation is restricted to trackers whose original source or binary codes are publicly available and uses default parameters from the source code; the work focuses on online single-target tracking, and the paper notes there is no single tracking approach that can successfully handle all scenarios.
- Evident: conclusions are tied to the 50-sequence dataset and its attribute thresholds, such as SV with \(t_s=2\), FM with \(t_m=20\), and LR with \(t_r=400\).
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Claims the paper makes
30 No basis given · 29 Own interpretation · 7 Own result · 5 Own method · 4 Not checked yet · 1 Sources not found
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In the paper
“We note that SCM, ASLA and LSK outperform MTT and L1APG.”
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In the paper
“Each tracker is extensively evaluated by analyzing more than 660,000 bounding box outputs.”
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In the paper
“In the precision plots, the rankings of some trackers are slightly different from the rankings in the success plots in that they are based on different metrics which measure different characteristics of trackers.”
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“On the other hand, the average performance of SRE is lower than that of OPE.”
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In the paper
“Additionally each sequence in the dataset is annotated with attributes that often affect tracking performance, such as occlusion, fast motion, and illumination variation.”
- Also stated in: § Abstract · p. 1 · plain
“The test image sequences are annotated with different attributes for performance evaluation and analysis.”
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- Also stated in: § Abstract · p. 1 · plain
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In the paper
“When an object moves fast, dense sampling based trackers (e.g., Struck, TLD and CXT) perform much better than others.”
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“Figure 6 illustrates the average performance of all trackers for each scale which shows the performance often decreases significantly when the scale factor is large (e.g., × 1 . 2 )”
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“The average TRE performance is higher than that of OPE”
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“To facilitate fair performance evaluation, we have collected and annotated most commonly used tracking sequences.”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Most sequences do not have the ground truth annotations, and the quantitative evaluation results may be generated with different initial conditions.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We build a tracking dataset with 50 fully annotated sequences to facilitate tracking evaluation.”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Most sequences do not have the ground truth annotations, and the quantitative evaluation results may be generated with different initial conditions.
- It may state two separate claims as one. 12
- Probably not a substantial claim. 26
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In this work, we build a code library that includes most publicly available trackers and a test dataset with ground-truth annotations to facilitate the evaluation task.”
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The input and output formats of most trackers are different and thus it is inconvenient for large scale performance evaluation.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Some attributes occur more frequently, e.g., OPR and IPR, than others.”
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“Aside from summarizing the performance on the whole dataset, we also construct several subsets corresponding to attributes to report specific challenging conditions.”
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“The AUC score of ASLA deceases less than the other top 5 trackers from OPE to SRE and the ranking of ASLA also increases.”
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In the paper
“In the success plots, the top ranked tracker SCM in OPE outperforms Struck by 2.6% but is 1.9% below Struck in SRE.”
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“At present, it includes 29 tracking algorithms.”
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In the paper
“The success plots of Struck in TRE and SRE show that the success rate of Struck is higher than SCM and ALSA when the overlap threshold is small, but less than SCM and ALSA when the overlap threshold is large.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The initial bounding boxes for tracking are sampled spatially and temporally to evaluate the robustness and characteristics of trackers.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- Rests on an assumption: The robustness to initialization is a well-known problem in the field, but it is seldom addressed in the literature.
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In the paper
“Because the AUC score of success plot measures the overall performance which is more accurate than the score at one threshold of the plot”
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None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“the target objects are usually humans or cars of small size in these surveillance sequences, and the background is usually static.”
What the sources say
- Not matchedcites [14] [14] R. Collins, X. Zhou, and S. K. Teh. An Open Source Tracking Testbed and Evaluation Web Site. In PETS , 2005. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [21] [21] R. B. Fisher. The PETS04 Surveillance Ground-Truth Data Sets. In PETS , 2004. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One common issue in assessing tracking algorithms is that the results are reported based on just a few sequences with different initial conditions or parameters.”
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In the paper
“Therefore, we propose two ways to analyze a tracker's robustness to initialization, by perturbing the initialization temporally (i.e., start at different frames) and spatially (i.e., start by different bounding boxes).”
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Probably not a substantial claim.
- Rests on an assumption: The robustness to initialization is a well-known problem in the field, but it is seldom addressed in the literature.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However a tracker may be sensitive to the initialization, and its performance with different initialization at a different start frame may become much worse or better.”
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Numerous factors affect the performance of a tracking algorithm, such as illumination variation, occlusion, as well as background clutters”
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In the paper
“For better evaluation and analysis of the strength and weakness of tracking approaches, we propose to categorize the sequences by annotating them with the 11 attributes shown in Table 2.”
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In the paper
“Thus, the results do not provide the holistic view of these algorithms.”
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results also show that OPE is not the best performance indicator as the OPE is one trial of SRE or TRE.”
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To the best of our knowledge, this is the largest scale performance evaluation of visual tracking.”
hedged- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For fair and comprehensive performance evaluation, we propose to perturb the initial state spatially and temporally from the ground-truth target locations.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
- Rests on an assumption: The robustness to initialization is a well-known problem in the field, but it is seldom addressed in the literature.
In the paper
“However, when the tracker loses the target, the output location can be random and the average error value may not measure the tracking performance correctly [6].”
What the sources say
- Not checked yetcites [6] [6] B. Babenko, M.-H. Yang, and S. Belongie. Robust Object Tracking with Online Multiple Instance Learning. PAMI , 33(7):1619-1632, 2011. - source
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In the paper
“For each tracker, the default parameters with the source code are used in all evaluations.”
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“there exists no single tracking approach that can successfully handle all scenarios.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“If these parameters are set to large values, more particles are required to make the tracker stable.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“To the best of our knowledge, this is the first comprehensive work to address and analyze the initialization problem of object tracking.”
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The robustness to initialization is a well-known problem in the field, but it is seldom addressed in the literature.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The initialization errors tend to cause trackers to update with imprecise appearance information, thereby causing gradual drifts.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: Trackers are often initialized by an object detector, which is likely to introduce initialization errors in terms of position and scale.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Evaluating trackers is difficult because many factors can affect the tracking performance.”
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Recently the precision plot [6, 27] has been adopted to measure the overall tracking performance.”
What the sources say
- Not checked yetcites [6] [6] B. Babenko, M.-H. Yang, and S. Belongie. Robust Object Tracking with Online Multiple Instance Learning. PAMI , 33(7):1619-1632, 2011. - source
- Not checked yetcites [27] [27] F. Henriques, R. Caseiro, P. Martins, and J. Batista. Exploiting the Circulant Structure of Tracking-by-Detection with Kernels. In ECCV , 2012. - source
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None of the sources cited for this claim have been checked yet.
In the paper
“Although object tracking has been studied for several decades, and much progress has been made in recent years [28, 16, 47, 5, 40, 26, 19], it remains a very challenging problem.”
What the sources say
- Not checked yetcites [28] [28] M. Isard and A. Blake. CONDENSATION-Conditional Density Propagation for Visual Tracking. IJCV , 29(1):5-28, 1998. - source
- Not checked yetcites [16] [16] D. Comaniciu, V. Ramesh, and P. Meer. Kernel-Based Object Tracking. PAMI , 25(5):564-577, 2003. - source
- Not checked yetcites [47] [47] D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang. Incremental Learning for Robust Visual Tracking. IJCV , 77(1):125-141, 2008. - source
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Improving these components will further advance the state of the art of online object tracking.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Third, motion model or dynamic model is crucial for object tracking, especially when the motion of target is large or abrupt.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Good location prediction based on the dynamic model could reduce the search range and thus improve the tracking efficiency and robustness.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This large scale performance evaluation facilitates better understanding of the state-of-the-art online object tracking approaches, and provides a platform for gauging new algorithms.”
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The evaluation results show that significant progress in the field of object tracking has been made in the last decade.”
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The robustness to initialization is a well-known problem in the field, but it is seldom addressed in the literature.
“While the robustness to initialization is a well-known problem in the field, it is seldom addressed in the literature.”
The paper relies on this without support.
Most sequences do not have the ground truth annotations, and the quantitative evaluation results may be generated with different initial conditions.
“However, most sequences do not have the ground truth annotations, and the quantitative evaluation results may be generated with different initial conditions.”
The paper relies on this without support.
The input and output formats of most trackers are different and thus it is inconvenient for large scale performance evaluation.
“However, the input and output formats of most trackers are different and thus it is inconvenient for large scale performance evaluation.”
The paper relies on this without support.
Trackers are often initialized by an object detector, which is likely to introduce initialization errors in terms of position and scale.
“as a tracker is often initialized by an object detector, which is likely to introduce initialization errors in terms of position and scale.”
The paper relies on this without support.
An object detector may be used to re-initialize a tracker at different time instances.
“In addition, an object detector may be used to re-initialize a tracker at different time instances.”
The paper relies on this without support.
TLD performs well in long sequences with a redetection module.
“This is because TLD performs well in long sequences with a redetection module while there are numerous short segments in TRE.”
The paper relies on this without support.
Struck only estimates the location of target and does not handle scale variation.
“This is because Struck only estimates the location of target and does not handle scale variation.”
The paper relies on this without support.
Sparse representations are used in SCM, ASLA, LSK, MTT and L1APG.
“Sparse representations are used in SCM, ASLA, LSK, MTT and L1APG.”
The paper relies on this without support.
The VTD and VTS methods adopt mixture models to improve the tracking performance.
“The VTD and VTS methods adopt mixture models to improve the tracking performance.”
The paper relies on this without support.
Trackers are often sensitive to initialization variations.
“It has been known that trackers are often sensitive to initialization variations.”
The paper relies on this without support.
The templates have to be warped to fit the size of the usually smaller canonical template.
“the templates have to be warped to fit the size of the usually smaller canonical template”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“TC-128 [34] contains 128 challenging videos.”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [34]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Out of these 128 videos, 78 videos are new, and others are repeated in other datasets.”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [34]
Experimental — accuracy not yet measured
Citations
Cites
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
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
(1981) · Cited by 11,963
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
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703
Yılmaz, A., Javed, O., & Shah, M. A. (2006) · ACM Computing Surveys · doi:10.1145/1177352.1177355 · Cited by 4,902
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
Research works printed but not linked: 5
- vojir, 0, Robustifying the flock of trackers, Proc Comput Vis Winter Workshop
- fisher, 0, The PETS04 surveillance ground-truth data sets, Proc IEEE Int Workshop Perform Eval Tracking Surveillance, 1
- ferryman, 0, PETS 2009: Dataset and challenge, Proc IEEE Int Workshop Perform Eval Tracking Surveillance, 1
- mei, 0, Robust visual tracking using l1 minimization, Proc 12th IEEE Int Conf Comput Vis, 1436
- collins, 0, An open source tracking testbed and evaluation web site, Proc IEEE Int Workshop Perform Eval Tracking Surveillance
Cited by
Sorted by citations
Items 901–1,000 of 1,002
UniMod1K: Towards a More Universal Large-Scale Dataset and Benchmark for Multi-modal Learning
Zhu, X., Xu, T., Liu, Z., Tang, Z., Wu, X., & Kittler, J. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-01999-8 · Cited by 17
Bidirectional Interaction of CNN and Transformer Feature for Visual Tracking
Sun, B., Wang, Z., Wang, S., Cheng, Y., & Ning, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3376690 · Cited by 19
Divert More Attention to Vision-Language Object Tracking
Guo, M., Zhang, Z., Jing, L., Ling, H., & Fan, H. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3409078 · Cited by 19
DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM
Li, X., Feng, X., Hu, S., Wu, M., Zhang, D., Zhang, J., & Huang, K. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00724 · Cited by 27
An anti-occlusion vision-based method for structural motion estimation
Hou, J., Zhang, Y., Lu, X., Cai, E., Wei, K., Luo, M., Guo, J. H., Ma, Z., Hoon Sohn, A., & Guo, T. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.112003 · Cited by 19
Spatial-temporal initialization dilemma: towards realistic visual tracking
Liu, C., Yuan, Y., Chen, X. Y., Lu, H., & Wang, D. (2024) · Visual Intelligence · doi:10.1007/s44267-024-00068-5 · Cited by 16
Data Association for Multi-Object Visual Tracking
Betke, M., & Wu, Z. (2017) · Synthesis lectures on computer vision · doi:10.1007/978-3-031-01816-9 · Cited by 18
RoI Pooling Based Fast Multi-Domain Convolutional Neural Networks for Visual Tracking
Qin, Y., He, S., Zhao, Y. H., & Gong, Y. (2016) · International Conference on Artificial Intelligence and Industrial Engineering (AIIE) · doi:10.2991/aiie-16.2016.46 · Cited by 16
Real‐time AdaBoost cascade face tracker based on likelihood map and optical flow
Ranftl, A., Alonso‐Fernandez, F., Karlsson, S. M., et al. (2017) · IET Biometrics · doi:10.1049/iet-bmt.2016.0202 · Cited by 16
Deep Learning and Preference Learning for Object Tracking: A Combined Approach
Pang, S., del Coz, J. J., Yu, Z., Luaces, O., & Díez, J. (2017) · Neural Processing Letters · doi:10.1007/s11063-017-9720-5 · Cited by 15
Cascaded Regional Spatio-Temporal Feature-Routing Networks for Video Object Detection
Shuai, H., Liu, Q., Zhang, K., Yang, J., & Deng, J. (2017) · IEEE Access · doi:10.1109/access.2017.2787155 · Cited by 15
Liang, W., Zhu, Y., & Zhu, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12222 · Cited by 16
Robust video tracking algorithm: a multi‐feature fusion approach
Wang, H., Nguang, S. K., & Wen, J. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2017.0404 · Cited by 16
Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling
Zhang, P., Yu, S., Xu, J., You, X., Jiang, X., Jing, X., & Tao, D. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2882339 · Cited by 15
Memory Mechanisms for Discriminative Visual Tracking Algorithms With Deep Neural Networks
Wang, L., Zhang, Y., Wang, J., & Zhang, Y. (2019) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2019.2900506 · Cited by 16
Deep Position-Sensitive Tracking
Zha, Y., Ku, T., Li, Y., & Zhang, P. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2922125 · Cited by 16
Application of gcForest to visual tracking using UAV image sequences
Liu, F., & Anzhe, 杨. Y. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07864-y · Cited by 14
Visual Tracking by Structurally Optimizing Pre-Trained CNN
Liu, C., Liu, P., Zhao, W., & Tang, X. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2938038 · Cited by 17
Contour-Aware Long-Term Tracking With Reliable Re-Detection
Tang, F., & Ling, Q. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2957748 · Cited by 16
Parallelization of the Honeybee Search Algorithm for Object Tracking
Perez-Cham, O. E., Puente, C., Soubervielle‐Montalvo, C., Gustavo Olague, A., Aguirre-Salado, C. A., & Núñez-Varela, A. S. (2020) · Applied Sciences · doi:10.3390/app10062122 · Cited by 16
STGL: Spatial-Temporal Graph Representation and Learning for Visual Tracking
Jiang, B., Zhang, Y., Luo, B., et al. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3008035 · Cited by 16
Occlusion detection and drift-avoidance framework for 2D visual object tracking
Karakostas, I., Mygdalis, V., Tefas, A., & Pitas, I. (2020) · Signal Processing Image Communication · doi:10.1016/j.image.2020.116011 · Cited by 14
TICNet: A Target-Insight Correlation Network for Object Tracking
Ruan, W., Ye, M., Wu, Y., Liu, W., Chen, J., Liang, C., Li, G., & Lin, C. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3070677 · Cited by 16
Self-citationSSPNet: Learning spatiotemporal saliency prediction networks for visual tracking
Lee, H., & Kim, S. (2021) · Information Sciences · doi:10.1016/j.ins.2021.06.042 · Cited by 15
CRACT: Cascaded Regression-Align-Classification for Robust Tracking
Fan, H., & Ling, H. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636803 · Cited by 17
Learning channel-aware deep regression for object tracking
Lu, X., Tang, F., Huo, H., et al. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.06.032 · Cited by 16
Correlation Filter With Motion Detection for Robust Tracking of Shape-Deformed Targets
Liu, C., Gong, J., Zhu, J., Zhang, J., & Yan, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2993777 · Cited by 16
Noise-Aware Framework for Robust Visual Tracking
Li, S., Zhao, S., Cheng, B. H., & Chen, J. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2996245 · Cited by 16
Razzaq, M. A., Medina-Quero, J., Cleland, I., Nugent, C., Akhtar, U., Bilal, H. S. M., Rehman, U. U., & Lee, S. (2020) · Multimedia Systems · doi:10.1007/s00530-020-00664-7 · Cited by 16
Occluded object tracking using object-background prototypes and particle filter
Mondal, A. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02047-x · Cited by 17
Rectified Binary Convolutional Networks with Generative Adversarial Learning
Liu, C., Ding, W., Hu, Y. C., Zhang, B., Liu, J., Guo, G., & Doermann, D. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01417-9 · Cited by 15
Mohammadi, M. R., Rahimzadeh, M., & Attar, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04034 · Cited by 24
Ocean: Object-aware Anchor-free TrackingPreprint
Zhang, Z., Peng, H., Fu, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10721 · Cited by 16
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and BenchmarkPreprint
Wang, X., Shu, X., Zhang, Z., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.16746 · Cited by 16
UncTrack: Reliable Visual Object Tracking With Uncertainty-Aware Prototype Memory Network
Yao, S., Guo, Y., Yan, Y., Ren, W., & Cao, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3559796 · Cited by 19
Robust Object Tracking via Large Margin and Scale-Adaptive Correlation Filter
Li, J., Zhou, X., Chan, S., & Chen, S. (2017) · IEEE Access · doi:10.1109/access.2017.2778740 · Cited by 15
Dilated-aware discriminative correlation filter for visual tracking
Xu, G., Zhu, H., Deng, L., Han, L., Li, Y., & Lu, H. (2018) · World Wide Web · doi:10.1007/s11280-018-0555-4 · Cited by 12
Shan, Y., Liu, S., Zhang, Y., Jing, M., & Xu, H. (2021) · IEEE Access · doi:10.1109/access.2021.3079132 · Cited by 16
Robust RGB-T Tracking via Graph Attention-Based Bilinear Pooling
Kang, B., Liang, D., Mei, J., Tan, X., Zhou, Q., & Zhang, D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3161969 · Cited by 16
Exploiting temporal coherence for self-supervised visual tracking by using vision transformer
Zhu, W., Wang, Z., Li, X., & Meng, J. H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109318 · Cited by 15
Robust visual tracking based on modified mayfly optimization algorithm
Xiao, Y., & Wu, Y. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104691 · Cited by 15
Self-citationVisualizing Skiers' Trajectories in Monocular Videos
Dunnhofer, M., Sordi, L., & Micheloni, C. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00547 · Cited by 16
Facing Completely Occluded Short-Term Tracking Based on Correlation Filters
Zhang, Y., Pan, H., Wang, J., & Sun, W. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3317483 · Cited by 19
BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision
Zhao, X., Hu, S., Wang, Y., Zhang, J., Hu, Y., Liu, R., Ling, H., Li, Y., Li, R., Liu, K., et al. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01937-0 · Cited by 15
Efficient correlation information mixer for visual object tracking
Chen, H., Zhang, W., Yan, D., Huang, L., & Yu, C. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111368 · Cited by 15
Spiking SiamFC++: deep spiking neural network for object tracking
Xiang, S., Zhang, T., Jiang, S., Han, Y., Zhang, Y., Guo, X., Yu, L., Shi, Y., & Hao, Y. (2024) · Nonlinear Dynamics · doi:10.1007/s11071-024-09525-8 · Cited by 19
The use of reinforcement learning algorithms in object tracking: A systematic literature review
Rojas, D., Medina, M. C. C., Fernandes, B., & Barros, P. V. A. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127954 · Cited by 16
Diff-Tracker: Text-to-Image Diffusion Models are Unsupervised Trackers
Zhang, Z., Xu, L., Peng, D., Rahmani, H., & Liu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73390-1_19 · Cited by 18
An Efficient Tracking System by Orthogonalized Templates
Yang, X., Wang, M., Zhang, L., Sun, F., Hong, R., & Qi, M. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2515559 · Cited by 16
Visual tracking via salient feature extraction and sparse collaborative model
Liu, Y. M., Feng, Y., Zhong, C., Tao, Y., Dai, B., & Yin, M. (2018) · AEU - International Journal of Electronics and Communications · doi:10.1016/j.aeue.2018.02.019 · Cited by 15
Learning Robust Gaussian Process Regression for Visual Tracking
Zheng, L., Tang, M., & Wang, J. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/170 · Cited by 15
MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
Lin, Y., Cheng, S., Shen, J., & Pantić, M. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756581 · Cited by 17
Revisiting Jump-Diffusion Process for Visual Tracking: A Reinforcement Learning Approach
Liu, X., Xu, Q., Chau, T., Mu, Y., Zhu, L., & Yan, S. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2862891 · Cited by 14
Visual object tracking with discriminative correlation filtering and hybrid color feature
Huang, Y., Zhao, Z., Wu, B., et al. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07901-w · Cited by 15
Fast-deepKCF Without Boundary Effect
Zheng, L., Tang, M., Chen, Y., Wang, J., & Lu, H. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00412 · Cited by 17
A new TLD target tracking method based on improved correlation filter and adaptive scale
Yang, X., Zhu, S., Xia, S., et al. (2019) · The Visual Computer · doi:10.1007/s00371-019-01772-w · Cited by 15
Kumar, N., & Sukavanam, N. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08501-4 · Cited by 15
Benchmarking Deep Trackers on Aerial Videos
Taufique, A. M. N., Minnehan, B., & Savakis, A. E. (2020) · Sensors · doi:10.3390/s20020547 · Cited by 16
A dense connection based network for real-time object tracking
Lu, Y., Yuan, Y., & Wang, Q. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.019 · Cited by 15
Reinforced Similarity Learning
Zhang, D., Zheng, Z., Li, M., et al. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413743 · Cited by 15
Integration of the 3D Environment for UAV Onboard Visual Object Tracking
Vujasinović, S., Becker, S., Breuer, T., Bullinger, S., Scherer‐Negenborn, N., & Arens, M. (2020) · Applied Sciences · doi:10.3390/app10217622 · Cited by 15
Wang, Z., Wang, J., & Fan, R. (2021) · IEEE Access · doi:10.1109/access.2021.3056105 · Cited by 14
IoU-guided Siamese region proposal network for real-time visual tracking
Zhou, L., He, Y. Q., Li, W., Mi, J., & Lei, B. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.111 · Cited by 15
A Unified Approach for Tracking UAVs in Infrared
Zhao, J., Zhang, X., & Zhang, P. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00141 · Cited by 16
Efficient Online Object Tracking Scheme for Challenging Scenarios
Mehmood, K., Ali, A., Jalil, A., Khan, B., Cheema, K. M., Murad, M., & Milyani, A. H. (2021) · Sensors · doi:10.3390/s21248481 · Cited by 15
Chaotic particle filter for visual object tracking
Firouznia, M., Faez, K., Amindavar, H. R., et al. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.02.014 · Cited by 15
Generating Reliable Online Adaptive Templates for Visual Tracking
Guo, J., Xu, T., Jiang, S., et al. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451440 · Cited by 15
Tracking by Instance Detection: A Meta-Learning ApproachPreprint
Wang, G., Luo, C., Sun, X., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00830 · Cited by 15
How to Train Your Energy-Based Model for RegressionPreprint
Gustafsson, F., Danelljan, M., Timofte, R., & Schön, T. B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.01698 · Cited by 20
Multi-modal Visual Tracking: Review and Experimental ComparisonPreprint
Zhang, P., Wang, D., & Lu, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.04176 · Cited by 15
Improved kernelized correlation filter tracking by using spatial regularization
Gao, L., Li, Y., & Ning, J. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.11.008 · Cited by 14
A four dukkha state-space model for hand tracking
Lim, K. M., Tan, A. W. C., & Tan, S. C. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.06.012 · Cited by 14
Robust object tracking based on adaptive multicue feature fusion
Kumar, A., Walia, G. S., & Sharma, K. (2020) · Journal of Electronic Imaging · doi:10.1117/1.jei.29.6.063001 · Cited by 14
A Twofold Convolutional Regression Tracking Network With Temporal and Spatial Mechanism
Li, X., Huang, L., & Wei, Z. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3077640 · Cited by 13
Robust Online Tracking With Meta-Updater
Zhao, J. W., Dai, K., Zhang, P., Wang, D., & Lu, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3202785 · Cited by 16
Bai, Y., Song, Y., Zhao, Y., Zhou, Y., Wu, X., He, Y., Zhang, Z., Yang, X., & Hao, Q. (2022) · Remote Sensing · doi:10.3390/rs14194756 · Cited by 15
DeepPCT: Single Object Tracking in Dynamic Point Cloud Sequences
Liu, H., Hu, Q., Ma, Y., Xie, K., & Guo, Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3232092 · Cited by 16
Robust Tracking via Learning Model Update With Unsupervised Anomaly Detection Philosophy
Gao, J., Zhong, B., & Chen, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3221723 · Cited by 15
Hierarchical memory-guided long-term tracking with meta transformer inquiry network
Wang, X., Nie, G., Li, B., Zhao, Y., Kang, M., & Liu, B. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110504 · Cited by 14
A Complex-Former Tracker With Dynamic Polar Spatio-Temporal Encoding
Li, X., Jiao, L., Zhu, H., Huang, Z., Liu, F., Li, L., Chen, P., & Yang, S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3302368 · Cited by 14
Robust Tracking via Bidirectional Transduction With Mask Information
Ning, T., Zhong, B., Liang, Q., Tang, Z., & Li, X. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3321497 · Cited by 14
Accelerating Robust-Object-Tracking via Level-3 BLAS-Based Sparse Learning
He, Z., Liang, H., Yang, S., Su, W., Wang, P., Lin, Z., Tan, B., & Xie, S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3343082 · Cited by 15
SDCS-CF: Saliency-driven localization and cascade scale estimation for visual tracking
Yang, P., Wang, Q., Dou, J., & Dou, L. (2023) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2023.104040 · Cited by 14
Visual tracking in camera-switching outdoor sport videos: Benchmark and baselines for skiing
Dunnhofer, M., & Micheloni, C. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.103978 · Cited by 14
CLBP for scale and orientation adaptive mean shift tracking
Sliti, O., Hamam, H., & Amiri, H. (2017) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2017.05.003 · Cited by 14
Wang, F., Lin, B., Zhang, J., et al. (2017) · Computers & Graphics · doi:10.1016/j.cag.2017.07.023 · Cited by 14
Robust Visual Tracking via Smooth Manifold Kernel Sparse Learning
Liu, G. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2844685 · Cited by 13
Zhu, Z., Liu, B., Rao, Y., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2903161 · Cited by 14
Correlation filter tracker with siamese: A robust and real-time object tracking framework
Pan, G., Chen, G., Kang, W., et al. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.05.033 · Cited by 14
DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking
Wu, Q., Yan, Y., Liang, Y., Liu, Y., & Wang, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20873-8_8 · Cited by 15
Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression
Kim, K.-R., Choi, W., Koh, Y. J., Jeong, S.-G., & Kim, C. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00036 · Cited by 14
Motion Guided Siamese Trackers for Visual Tracking
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Metadata
- Authors
- Wu, Y., Lim, J., & Yang, M.-H.
- Affiliations
- Nanjing University of Information Science and Technology (CN)
- Hanyang University (KR)
- University of California, Merced (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2015-09-01
- Volume
- 37
- Issue
- 9
- Pages
- 1834-1848
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2158592639
- Semantic Scholar
- c4c45661501c16064eead6e5d37dcb80d41c7a78
- Cited by
- 3,371
- References
- 118
- Citations by year
- 2026: 102 · 2025: 174 · 2024: 209 · 2023: 295 · 2022: 323 · 2021: 442 · 2020: 483 · 2019: 539 · 2018: 429 · 2017: 235 · 2016: 116 · 2015: 24
- FWCI
- 230.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- The Royal Society of Chemistry’s Journals, Books and Databases (The Royal Society of Chemistry) (submittedVersion)
- Funders
- NSFC (61005027, 61370036)
- ICT R&D programs of MSIP/IITP (10047078, 14-824-09-006)
- National Science Foundation (1149783)
- IIS (1152576)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- object tracking, benchmark dataset, tracking algorithm evaluation, online tracking, performance evaluation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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