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2009
Springer

Analyzing Trajectories Using Uncertainty and Background Information

8 years 11 months ago
Analyzing Trajectories Using Uncertainty and Background Information
A key issue in clustering data, regardless the algorithm used, is the definition of a distance function. In the case of trajectory data, different distance functions have been proposed, with different degrees of complexity. All these measures assume that trajectories are error-free, which is essentially not true. Uncertainty is present in trajectory data, which is usually obtained through a series of GPS of GSM observations. Trajectories are then reconstructed, typically using linear interpolation. A well-known model to deal with uncertainty in a trajectory sample, uses the notion of space-time prisms (also called beads), to estimate the positions where the object could have been, given a maximum speed. Thus, we can replace a (reconstructed) trajectory by a necklace (intuitively, a a chain of prisms), connecting consecutive trajectory sample points. When it comes to clustering, the notion of uncertainty requires appropriate distance functions. The main contribution of this paper is t...
Bart Kuijpers, Bart Moelans, Walied Othman, Alejan
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where SSD
Authors Bart Kuijpers, Bart Moelans, Walied Othman, Alejandro A. Vaisman
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