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CONDENSATION—Conditional Density Propagation for Visual Tracking
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“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.”
From Object Tracking Benchmark (2015) · cites this paper as [28]
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Metadata
- Authors
- Isard, M., & Blake, A.
- Affiliations
- University of Oxford (GB)
- ORCID
- Blake, Andrew
- Venue
- International Journal of Computer Vision
- Published
- 1998-08
- Volume
- 29
- Issue
- 1
- Pages
- 5-28
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2161406034
- Cited by
- 4,886
- References
- 55
- Citations by year
- 2026: 13 · 2025: 13 · 2024: 17 · 2023: 17 · 2022: 29 · 2021: 39 · 2020: 44 · 2019: 61 · 2018: 77 · 2017: 133 · 2016: 152 · 2015: 200 · 2014: 268 · 2013: 298 · 2012: 319
- FWCI
- 53.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- visual object tracking, curve tracking, condensation algorithm, non-Gaussian distributions, multiple hypothesis tracking
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