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ADMI
2010
Springer

Probabilistic Modeling of Mobile Agents' Trajectories

10 years 4 months ago
Probabilistic Modeling of Mobile Agents' Trajectories
Abstract. We present a method for learning characteristic motion patterns of mobile agents. The method works on two levels. On the first level, it uses the expectation-maximization algorithm to build a Gaussian mixture model of the spatial density of agents' movement. On the second level, agents' trajectories as expressed as sequences of the components of the mixture model; the sequences are subsequently used to train hidden Markov models. The trained hidden Markov models are then employed to determine agent type, predict further agent movement or detect anomalous agents. The method has been evaluated in the maritime domain using ship trajectory data generated by the AgentC maritime traffic simulation.
Stepán Urban, Michal Jakob, Michal Pechouce
Added 26 Oct 2010
Updated 26 Oct 2010
Type Conference
Year 2010
Where ADMI
Authors Stepán Urban, Michal Jakob, Michal Pechoucek
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