Teetool -- A Probabilistic Trajectory Analysis Tool
Teetool -- A Probabilistic Trajectory Analysis Tool
Teetool is a Python package which models and visualises motion patterns found in two- and three-dimensional trajectory data. It models the trajectories as a Gaussian process and uses the mean and covariance of the trajectory data to produce a confidence region, an area (or volume) through which a given percentage of trajectories travel.
The confidence region is useful in obtaining an understanding of, or quantifying, dispersion in trajectory data. Furthermore, by modelling the trajectories as a Gaussian process, missing data can be recovered and noisy measurements can be corrected.
Teetool is available as a Python package on GitHub. The project includes Jupyter Notebooks, showing examples for two- and three-dimensional trajectory data.
trajectory patterns, Gaussian process, motion patterns, confidence region, Python
Eerland, Willem
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Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Fangohr, Hans
9b7cfab9-d5dc-45dc-947c-2eba5c81a160
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Eerland, Willem
7f5826c3-536f-4fdc-955e-0f9870c96a0e
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Fangohr, Hans
9b7cfab9-d5dc-45dc-947c-2eba5c81a160
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Eerland, Willem, Box, Simon, Fangohr, Hans and Sobester, Andras
(2017)
Teetool -- A Probabilistic Trajectory Analysis Tool.
Zenodo
doi:10.5281/ZENODO.251481
[Dataset]
Abstract
Teetool is a Python package which models and visualises motion patterns found in two- and three-dimensional trajectory data. It models the trajectories as a Gaussian process and uses the mean and covariance of the trajectory data to produce a confidence region, an area (or volume) through which a given percentage of trajectories travel.
The confidence region is useful in obtaining an understanding of, or quantifying, dispersion in trajectory data. Furthermore, by modelling the trajectories as a Gaussian process, missing data can be recovered and noisy measurements can be corrected.
Teetool is available as a Python package on GitHub. The project includes Jupyter Notebooks, showing examples for two- and three-dimensional trajectory data.
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More information
Published date: 2017
Keywords:
trajectory patterns, Gaussian process, motion patterns, confidence region, Python
Identifiers
Local EPrints ID: 433786
URI: http://eprints.soton.ac.uk/id/eprint/433786
PURE UUID: be04824c-844e-49c2-a22c-d2ca2bd5427f
Catalogue record
Date deposited: 03 Sep 2019 16:31
Last modified: 06 May 2023 01:39
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Contributors
Creator:
Willem Eerland
Creator:
Simon Box
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