Clustering of vehicle trajectories

Authors:
Stefan Atev;Grant Miller;Nikolaos P. Papanikolopoulos
Affiliations:
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN and Vital Images, Inc., Minnetonka, MN;Concrete Software Inc., Eden Prairie, MN and Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN;Department of Computer Science and Engineering and Security in Transportation Technology Research and Applications, Center for Transportation Studies, University of Minnesota, Minneapolis, MN
Venue:
IEEE Transactions on Intelligent Transportation Systems
Year:
2010

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Abstract

We present a method that is suitable for clustering of vehicle trajectories obtained by an automated vision system. We combine ideas from two spectral clustering methods and propose a trajectory-similarity measure based on the Hausdorff distance, with modifications to improve its robustness and account for the fact that trajectories are ordered collections of points. We compare the proposed method with two well-known trajectory-clustering methods on a few real-world data sets.