Model-based classification of trajectories

Abstract

We present algorithms for classifying trajectories based on a movement model parameterized by a single parameter, like the Brownian bridge movement model. Classification is the problem of assigning trajectories to classes of similar movement characteristics. For instance, the set of trajectories might be the subtrajectories resulting from segmenting a trajectory, thus identifying movement phases. We give an efficient algorithm to compute the optimal classification for a discrete set of parameter values. 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.