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Dynamics-Aware Similarity of Moving Objects Trajectories (2007)
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BibTeX
@MISC{Trajcevski07dynamics-awaresimilarity,
author = {Goce Trajcevski and Hui Ding and Peter Scheuermann and Roberto Tamassia and Dennis Vaccaro},
title = {Dynamics-Aware Similarity of Moving Objects Trajectories },
year = {2007}
}
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Abstract
This work addresses the problem of obtaining the degree of similarity between trajectories of moving objects. Typically, a Moving Objects Database (MOD) contains sequences of (location,time) points describing the motion of individual objects, however, they also have an implicit information about the velocity, which is an important attribute describing the dynamics of a particular object. Our main goal is to extend the MOD functionalities with the capability of reasoning about how similar are the trajectories of objects that, possibly, move along geographically different routes. Towards this, we use a distance function which balances the lack of temporal-awareness of the Hausdorff distance with the generality (and complexity of calculation) of the Fréchet distance. Based on the observation that, as a firstapproximation in practice, the individual segments of trajectories are assumed to have constant speed, we provide efficient algorithms for: (1) optimal matching between trajectories; and (2) approximate matching between trajectories, both under translations and rotations, where the approximate algorithm guarantees a bounded error-quality with respect to the optimal one.
Keyphrases
dynamics-aware similarity moving object trajectory important attribute hausdorff distance approximate algorithm distance function main goal efficient algorithm moving object database individual object individual segment different route constant speed bounded error-quality mod functionality fr chet distance implicit information particular object
