The DOI matches its Crossref record; 65 of the 68 references it cites were found in a registry or an index.
Online Object Tracking: A Benchmark
Abstract
Object tracking is one of the most important components in numerous applications of computer vision. While much progress has been made in recent years with efforts on sharing code and datasets, it is of great importance to develop a library and benchmark to gauge the state of the art. After briefly reviewing recent advances of online object tracking, we carry out large scale experiments with various evaluation criteria to understand how these algorithms perform. The test image sequences are annotated with different attributes for performance evaluation and analysis. By analyzing quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.
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“OTB-100 [33] contains 100 different challenging videos.”
From Multiple Cues-Based Robust Visual Object Tracking Method (2022) · cites this paper as [33]
Citations
Cites
Histograms of Oriented Gradients for Human Detection
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Baker, S., & Matthews, I. A. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000011205.11775.fd · Cited by 3,159
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Self-citationLearning to detect natural image boundaries using local brightness, color, and texture cues
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Flagged first, then by citations
3 of the 68 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- mei, 2009, Robust visual tracking using l1 minimization, ICCV
- collins, 2005, An open source tracking testbed and evaluation web site, PETS
- fisher, 2004, The pets04 surveillance ground-truth data sets, PETS
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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
Yang, X., Li, S., Yu, J., Zhang, K., Yang, J., & Yan, J. (2021) · Infrared Physics & Technology · doi:10.1016/j.infrared.2021.103958 · Cited by 14
On the improvement of foreground–background model‐based object tracker
Hanif, M. S., Ahmad, S., & Khurshid, K. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0487 · Cited by 14
Multiple Cues-Based Robust Visual Object Tracking Method
Khan, B., Jalil, A., Ali, A., Alkhaledi, K., Mehmood, K., Cheema, K. M., Murad, M., Tariq, H., & El-Sherbeeny, A. M. (2022) · Electronics · doi:10.3390/electronics11030345 · Cited by 5
“Twenty-three videos were selected from three standard datasets, i.e., OTB-50 [8], OTB-100 [9], and Temple Colour-128 [10].”
Influential
Metadata
- Authors
- Wu, Y., Lim, J., & Yang, M.-H.
- Affiliations
- University of California, Merced (US)
- Hanyang University (KR)
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 2411-2418
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2089961441
- Semantic Scholar
- bfba194dfd9c7c27683082aa8331adc4c5963a0d
- Cited by
- 4,138
- References
- 68
- Citations by year
- 2026: 51 · 2025: 161 · 2024: 206 · 2023: 258 · 2022: 314 · 2021: 351 · 2020: 413 · 2019: 541 · 2018: 507 · 2017: 429 · 2016: 401 · 2015: 318 · 2014: 167 · 2013: 21
- FWCI
- 192.43
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- 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
- online object tracking, benchmark, evaluation criteria, robust tracking
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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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