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Unsupervised trajectory compression

Corcoran, Padraig, Mooney, Peter and Huang, Guoquan 2016. Unsupervised trajectory compression. Presented at: The International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 16-21 May 2016. The International Conference on Robotics and Automation (ICRA). IEEE, 10.1109/ICRA.2016.7487479

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Abstract

We present a method for compressing trajectories in an unsupervised manner. Given a set of trajectories sampled from a space we construct a basis for compression whose elements correspond to paths in the space which are topologically distinct. This is achieved by computing a canonical representative for each element in a generating set for the first homology group and decomposing these representatives into a set of distinct paths. Trajectory compression is subsequently accomplished through representation in terms of this basis. Robustness with respect to outliers is achieved by only considering those elements of the first homology group which exist in the super-level sets of the Kernel Density Estimation (KDE) above a threshold. Robustness with respect to small scale topological artifacts is achieved by only considering those elements of the first homology group which exist for a sufficient range in the super-level sets. We demonstrate this approach to trajectory compression in the context of a large set of crowd-sourced GPS trajectories captured in the city of Chicago. On this set, the compression method achieves a mean geometrical accuracy of 108 meters with a compression ratio of over 12.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IEEE
ISBN: 978-1-4673-8026-3
Date of First Compliant Deposit: 30 March 2016
Last Modified: 02 Aug 2019 11:00
URI: http://orca.cf.ac.uk/id/eprint/86952

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