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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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 note that SCM, ASLA and LSK outperform MTT and L1APG.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“Each tracker is extensively evaluated by analyzing more than 660,000 bounding box outputs.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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
“On the other hand, the average performance of SRE is lower than that of OPE.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
“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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Abstract · p. 1 · plain
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
“When an object moves fast, dense sampling based trackers (e.g., Struck, TLD and CXT) perform much better than others.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“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 )”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
“The average TRE performance is higher than that of OPE”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
“To facilitate fair performance evaluation, we have collected and annotated most commonly used tracking sequences.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- 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.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“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 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
“The AUC score of ASLA deceases less than the other top 5 trackers from OPE to SRE and the ranking of ASLA also increases.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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 the success plots, the top ranked tracker SCM in OPE outperforms Struck by 2.6% but is 1.9% below Struck in SRE.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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
“At present, it includes 29 tracking algorithms.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“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.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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
“The initial bounding boxes for tracking are sampled spatially and temporally to evaluate the robustness and characteristics of trackers.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.
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
“Because the AUC score of success plot measures the overall performance which is more accurate than the score at one threshold of the plot”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
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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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).”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
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”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
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 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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In the paper
“Thus, the results do not provide the holistic view of these algorithms.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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.”
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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 each tracker, the default parameters with the source code are used in all evaluations.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
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
“If these parameters are set to large values, more particles are required to make the tracker stable.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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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.”
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
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.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
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
“Third, motion model or dynamic model is crucial for object tracking, especially when the motion of target is large or abrupt.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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
“Good location prediction based on the dynamic model could reduce the search range and thus improve the tracking efficiency and robustness.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
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.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
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.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
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Robust Real-Time Face Detection
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(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
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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
Online Object Tracking: A Benchmark
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“In recent years, many benchmark datasets have been developed for various vision problems, such as the Berkeley segmentation [38], FERET face recognition [45] and optical flow dataset [9].”
BackgroundRobust object tracking with online multiple instance learning
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Self-citationRobust Fragments-based Tracking using the Integral Histogram
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Self-citationMean-shift blob tracking through scale space
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Elliptical head tracking using intensity gradients and color histograms
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A survey of appearance models in visual object tracking
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A Database and Evaluation Methodology for Optical Flow
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Covariance Tracking using Model Update Based on Lie Algebra
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Distribution fields for tracking
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Context tracker: Exploring supporters and distracters in unconstrained environments
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Transfer Learning Based Visual Tracking with Gaussian Processes Regression
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Robust Visual Tracking via Structured Multi-Task Sparse Learning
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Self-citationSpatiograms versus Histograms for Region-Based Tracking
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Self-citationNotice of Removal: The Visual Object Tracking VOT2013 Challenge Results
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Robust Object Tracking via Sparse Collaborative Appearance Model
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Self-citationSparse coding based visual tracking: Review and experimental comparison
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Least Soft-Threshold Squares Tracking
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Self-citationCo-Tracking Using Semi-Supervised Support Vector Machines
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Self-citationOnline Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
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Tracking the invisible: Learning where the object might be
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Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking
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Adaptive Appearance Modeling for Video Tracking: Survey and Evaluation
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Online robust image alignment via iterative convex optimization
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Self-citationPETS2010: Dataset and Challenge
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Visual Tracking via Probability Continuous Outlier Model
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Visual tracking with histograms and articulating blocks
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Self-citationRobust tracking via weakly supervised ranking SVM
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Real-Time Probabilistic Covariance Tracking With Efficient Model Update
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Self-citationCannons, K. J. (2008) · Cited by 106
Blurred target tracking by Blur-driven Tracker
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Self-citationDiscriminative Spatial Attention for Robust Tracking
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Self-citationVisual Tracking via Online Nonnegative Matrix Factorization
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Tracking of Abrupt Motion Using Wang-Landau Monte Carlo Estimation
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An experimental comparison of online object-tracking algorithms
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Self-citationLearning to track with multiple observers
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Online Object Tracking, Learning, and Parsing with And-Or Graphs
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Self-citationOnline Empirical Evaluation of Tracking Algorithms
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Flagged first, then by citations
21 of the 118 references couldn’t be matched to a known paper.
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
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Cited by
Sorted by citations
High Performance Visual Tracking with Siamese Region Proposal Network
Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00935 · Cited by 3,023
“OTB2015 [37] contains 100 sequences that are collected from commonly used tracking sequences.”
BackgroundMethodLearning Multi-domain Convolutional Neural Networks for Visual Tracking
Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806
“(b) OTB100 result Figure 3: Precision and success plots on OTB50 [41] and OTB100 [40].”
BackgroundMethodResultInfluentialECO: Efficient Convolution Operators for Tracking
Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.733 · Cited by 2,805
“We validate our proposed formulation by performing comprehensive experiments on four benchmarks: VOT2016 [23], UAV123 [29], OTB-2015 [37], and TempleColor [26].”
BackgroundMethodInfluentialSiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks
Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., & Yan, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00441 · Cited by 2,607
“The standardized benchmarks [47, 48, 10] provide fair testbeds for comparisons with different algorithms.”
BackgroundMethodInfluentialHierarchical Convolutional Features for Visual Tracking
Ma, C., Huang, J., Yang, X., & Yang, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.352 · Cited by 1,819
Self-citation
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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