DIOT: Detecting Implicit Obstacles from Trajectories

Yifan Lei, Qiang Huang, Mohan S. Kankanhalli, Anthony K. H. Tung

Abstract: In this paper, we study a new data mining problem of obstacle detection. Intuitively, given two kinds of trajectories, i.e., reference and query trajectories, the obstacle is a region such that most query trajectories bypass this region, whereas the reference trajectories go through as usual. We introduce a density-based definition for the obstacle within a new normalized Dynamic Time Warping distance and the density functions tailored for the sub-trajectories to estimate the density variations. With this definition, we introduce a novel framework DIOT to detect implicit obstacles. Experimental results show that DIOT can capture the nature of obstacles yet detect the obstacles efficiently and effectively.