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Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects

Dodge, S., Weibel, R. and Forootan, Ehsan 2009. Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects. Journal of Computers, Environment and Urban System 33 , pp. 419-434.

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Abstract

We propose a segmentation and feature extraction method for trajectories of moving objects. The methodology consists of three stages: trajectory data preparation; global descriptors computation; and local feature extraction. The key element is an algorithm that decomposes the profiles generated for different movement parameters (velocity, acceleration, etc.) using variations in sinuosity and deviation from the median line. Hence, the methodology enables the extraction of local movement features in addition to global ones that are essential for modeling and analyzing moving objects in applications such as trajectory classification, simulation and extraction of movement patterns. As a case study, we show how the method can be employed in classifying trajectory data generated by unknown moving objects and assigning them to known types of moving objects, whose movement characteristics have been previously learned. We have conducted a series of experiments that provide evidence about the similarities and differences that exist among different types of moving objects. The experiments show that the methodology can be successfully applied in automatic transport mode detection. It is also shown that eye-movement data cannot be successfully used as a proxy of full-body movement of humans, or vehicles.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Earth and Ocean Sciences
Uncontrolled Keywords: Moving point data mining; Moving object; Movement parameters; Movement behavior; Trajectory decomposition; Trajectory classification
Publisher: Elsevier
Date of First Compliant Deposit: 17 October 2016
Date of Acceptance: 2009
Last Modified: 06 May 2019 21:26
URI: http://orca.cf.ac.uk/id/eprint/94865

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