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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]
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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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