Particle Filter Tracking
Particle Filter Tracking is a sequential Monte Carlo method for visual tracking that recursively estimates the posterior distribution of a target's state by representing it with a set of weighted samples, or particles, propagated over time according to a dynamic model and updated based on observation likelihood.
1.4K
Publications
73.3K
Citations
3.6K
Authors
1.1K
Institutions
Publications per year
2017–2026
249
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Defence Science and Technology Group
Canberra, Australia
20 h-index
39 publications
15.1K citations
University of Cambridge
Cambridge, United Kingdom
19 h-index
44 publications
26.9K citations
Tsinghua University
Beijing, China
19 h-index
104 publications
4.1K citations
Qinetiq (United Kingdom)
Farnborough, United Kingdom
19 h-index
37 publications
25.9K citations

Chinese Academy of Sciences
Beijing, China
19 h-index
74 publications
4K citations
University of Maryland, College Park
College Park, United States
18 h-index
55 publications
5.1K citations
Georgia Institute of Technology
Atlanta, United States
18 h-index
48 publications
5.3K citations

Centre National de la Recherche Scientifique
Paris, France
17 h-index
68 publications
2.5K citations
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23 publications · 1.6K citations
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19 publications · 13K citations
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26 publications · 1K citations
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16 publications · 1.6K citations
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17 publications · 1K citations
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