Robust Time-Referenced Segmentation of Moving Object Trajectories

Trajectory segmentation is the process of partitioning a given trajectory into a small number of homogeneous segments w.r.t. some criteria. Conventional segmentation techniques only focus on the spatial features of the movement and could lead to spatially homogeneous segments but with presumably dissimilar temporal structures. Furthermore, trajectories could be over-segmented in the presence of outliers. In this paper, we propose a family of three trajectory segmentation methods that takes into account both geospatial and temporal structures of movement for the segmentation and is also robust with respect to time-referenced spatial outliers. The effectiveness of our methods is empirically demonstrated over three real-world datasets.