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Capturing and Explaining Trajectory Singularities using Composite Signal Neural Networks

Dubois, Hippolyte; Le Callet, Patrick; Hornberger, Michael; Spiers, Hugo J; Coutrot, Antoine; (2020) Capturing and Explaining Trajectory Singularities using Composite Signal Neural Networks. In: Proceedings of the 2020 28th European Signal Processing Conference (EUSIPCO). (pp. pp. 1422-1426). Institute of Electrical and Electronics Engineers (IEEE) Green open access

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Abstract

Spatial trajectories are ubiquitous and complex signals. Their analysis is crucial in many research fields, from urban planning to neuroscience. Several approaches have been proposed to cluster trajectories. They rely on hand-crafted features, which struggle to capture the spatio-temporal complexity of the signal, or on Artificial Neural Networks (ANNs) which can be more efficient but less interpretable. In this paper we present a novel ANN architecture designed to capture the spatio-temporal patterns characteristic of a set of trajectories, while taking into account the demographics of the navigators. Hence, our model extracts markers linked to both behaviour and demographics. We propose a composite signal analyser (CompSNN) combining three simple ANN modules. Each of these modules uses different signal representations of the trajectory while remaining interpretable. Our CompSNN performs significantly better than its modules taken in isolation and allows to visualise which parts of the signal were most useful to discriminate the trajectories.

Type: Proceedings paper
Title: Capturing and Explaining Trajectory Singularities using Composite Signal Neural Networks
Event: 28th European Signal Processing Conference (EUSIPCO)
Location: Amsterdam, Netherlands
Dates: 18th-22nd January 2021
ISBN-13: 978-9-08279-704-6
Open access status: An open access version is available from UCL Discovery
DOI: 10.23919/Eusipco47968.2020.9287403
Publisher version: https://doi.org/10.23919/Eusipco47968.2020.9287403
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: graph signal processing, neural network, cnn, gcnn, explainability, trajectory, pattern analysis
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10143863
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