Autonomous crowds tracking with box particle filtering and convolution particle filtering
- Submitting institution
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The University of Hull
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1400126
- Type
- D - Journal article
- DOI
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10.1016/j.automatica.2016.03.009
- Title of journal
- Automatica : the journal of IFAC, the International Federation of Automatic Control
- Article number
- -
- First page
- 380
- Volume
- 69
- Issue
- -
- ISSN
- 0005-1098
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- URL
-
https://www.sciencedirect.com/science/article/pii/S0005109816300887?via%3Dihub
- 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
-
4
- Research group(s)
-
-
- Citation count
- 23
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Autonomous systems such as Unmanned Aerial Vehicles (UAVs) need to be able to recognise and track crowds of people, e.g. for rescuing and surveillance purposes. This paper derives a box particle (new fast and robust Monte Carlo Sampling technique) solution for Crowd tracking using a freely available code (available at the University of Sheffield https://figshare.shef.ac.uk/). This paper, within Bayesian framework, offers a robust and compressed representation of information which is able to circumvent the data association problem where the crowd size measurement data is uncertain. It is funded by EU FP7 TRAX.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -