Discovering Semantic Spatial and Spatio-Temporal Outliers from Moving
  Object Trajectories
  Several algorithms have been proposed for discovering patterns from
trajectories of moving objects, but only a few have concentrated on outlier
detection. Existing approaches, in general, discover spatial outliers, and do
not provide any further analysis of the patterns. In this paper we introduce
semantic spatial and spatio-temporal outliers and propose a new algorithm for
trajectory outlier detection. Semantic outliers are computed between regions of
interest, where objects have similar movement intention, and there exist
standard paths which connect the regions. We show with experiments on real data
that the method finds semantic outliers from trajectory data that are not
discovered by similar approaches.
