Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking
- Submitting institution
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The University of Hull
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 3402389
- Type
- D - Journal article
- DOI
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10.1016/j.dsp.2013.11.006
- Title of journal
- Digital Signal Processing
- Article number
- -
- First page
- 1
- Volume
- 25
- Issue
- -
- ISSN
- 1051-2004
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2014
- URL
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https://www.sciencedirect.com/science/article/pii/S1051200413002716
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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5
- Research group(s)
-
-
- Citation count
- 98
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Multi-object tracking is a well-studied problem having relevance in virtually any application requiring environment perception. Consequently interest in tracking a number of objects moving in a coordinated and interacting fashion is increasing. This paper is a review of several works on group / extended objects tracking and stemmed from various UK projects (DIF-DTC, EPRSC) and FP7 EU project. These various works helped to successfully secure a grant for the EC Seventh Framework Programme [FP7 2013–2017] TRAcking in compleX sensor systems (TraX) lead by the first author. This paper presents five potential trends of modelling objects within the Bayesian framework.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -