Crowd Tracking.zip (55.44 kB)
Algorithms and models for autonomous crowds tracking with box particle filtering and convolution particle filtering
Version 2 2016-09-02, 13:26
Version 1 2016-08-23, 08:33
software
posted on 2016-09-02, 13:26 authored by Allan De FreitasAllan De Freitas, Lyudmila MihaylovaLyudmila MihaylovaModels and algorithms for autonomous crowds tracking are developed. The algorithms are: a Convolutional particle filter, a Box particle filter and a sampling importance resampling (SIR) particle filter. The programs are modular and self-contained.
Code linked to the paper: "Autonomous Crowds Tracking with Box Particle Filtering and Convolution Particle Filtering", Automatica, vol. 69, pp. 380-394, July 2016.
The Convolutional particle filter and the SIR particle filter are implemented and compared with the Box particle filter in a modular way. The Box particle filter requires a library called Intlab for operation.
This library can be purchased and downloaded from: http://www.ti3.tu-harburg.de/rump/intlab/
Funding
Bayesian Tracking and Reasoning over Time grant EP/K021516/1 (R/138616-11-1);Tracking in Complex Sensor Systems grant 607400 (R/138948-11-1)
History
Ethics
- There is no personal data or any that requires ethical approval
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- The data complies with the institution and funders' policies on access and sharing
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- The data can be shared openly
Data description
- The file formats are open or commonly used
Methodology, headings and units
- Headings and units are explained in the files
