Detection of unusual trajectories using multi-objective evolutionary algorithms and rough sets
2013
Detection of unusual trajectories of moving objects (e.g., people, automobiles, etc.) is an important problem
in many civilian and military surveillance applications. In this work, we propose a multi-objective evolutionary
algorithms and rough sets-based approach that breaks down 2-dimensional trajectories into a set
of additive components, which then can be used to build a classifier capable of recognizing typical, but yet
unseen trajectories, and identifying those that seem suspicious.
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