Crowd Tracking.zip (55.44 kB)

Algorithms and models for autonomous crowds tracking with box particle filtering and convolution particle filtering

Download (98.14 kB)
software
posted on 02.09.2016, 13:26 by Allan De Freitas, Lyudmila Mihaylova
Models 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

Policy

The data complies with the institution and funders' policies on access and sharing

Sharing and access restrictions

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

Licence

Exports

Logo branding

Licence

Exports