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Model-Based Segmentation and Classification of Trajectories

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We present efficient algorithms for segmenting and classifying trajectories based on a movement model parameterised by a single parameter, like the Brownian bridge movement model. Segmentation is the problem of subdividing a trajectory into interior-disjoint parts such that each part is homogeneous in its movement characteristics. We formalise this using the likelihood of the model parameter, and propose a new algorithm for trajectory segmentation based on this. We consider the case where a discrete set of m parameter values is given and present an algorithm to compute an optimal segmentation with respect to an information criterion in O(nm) time for a trajectory with n sampling points. We also present an algorithm that efficiently computes the optimal segmentation if we allow the parameter values to be drawn from a continuous domain. Classification is the problem of assigning trajectories to classes of similar movement characteristics. The set of trajectories might for instance be the subtrajectories resulting from segmenting a trajectory, thus identifying movement phases. We give an algorithm to compute the optimal classification with respect to an information criterion in \(O(m^2 + km\log m)\) time for m parameter values and k trajectories, assuming bitonic likelihood functions. We also show that classification is NP-hard if the parameter values are allowed to vary continuously and present an algorithm that solves the problem in polynomial time under mild assumptions on the input.

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    For this they introduce a new model for similarity. The term model is used in our paper in a different sense, namely as referring to statistical models.


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Correspondence to Stef Sijben.

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Alewijnse, S.P.A., Buchin, K., Buchin, M. et al. Model-Based Segmentation and Classification of Trajectories. Algorithmica 80, 2422–2452 (2018). https://doi.org/10.1007/s00453-017-0329-x

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  • Trajectory analysis
  • Segmentation
  • Classification
  • Movement model