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Cited by 3,371Video Surveillance and Tracking MethodsAutonomous Vehicle Technology and SafetyAdvanced Neural Network Applications

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

Wu, Yi, Lim, Jongwoo, Yang, Ming-Hsuan·2015·IEEE Transactions on Pattern Analysis and Machine Intelligence
DOI 10.1109/tpami.2014.2388226
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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 \(t_r=400\).

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1109/tpami.2014.2388226

Cites

97 of 97 references verified0 citation errors · 0 not checkable

Statistics

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Verification

Claims the paper makes

0 of 11 cited sources checked · 0 supported · 0 flagged

Show all 43 claims

  • It may state two separate claims as one. 12
  • Probably not a substantial claim. 26
33 of 33

  • 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”
11 of 11

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Citations

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21 of the 118 references couldn’t be matched to a known paper.

Research works printed but not linked: 5

  1. vojir, 0, Robustifying the flock of trackers, Proc Comput Vis Winter Workshop
  2. fisher, 0, The PETS04 surveillance ground-truth data sets, Proc IEEE Int Workshop Perform Eval Tracking Surveillance, 1
  3. ferryman, 0, PETS 2009: Dataset and challenge, Proc IEEE Int Workshop Perform Eval Tracking Surveillance, 1
  4. mei, 0, Robust visual tracking using l1 minimization, Proc 12th IEEE Int Conf Comput Vis, 1436
  5. collins, 0, An open source tracking testbed and evaluation web site, Proc IEEE Int Workshop Perform Eval Tracking Surveillance

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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)
ORCID
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2015-09-01
Volume
37
Issue
9
Pages
1834-1848
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
DOI
10.1109/tpami.2014.2388226
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
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
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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