期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 11, 期 3, 页码 647-657出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2010.2048101
关键词
Clustering of trajectories; time-series similarity measures; unsupervised learning
资金
- U.S. Army Research Laboratory
- U.S. Army Research Office [911NF-08-1-0463, 55111-CI]
- National Science Foundation [IIS-0219863, IIP-0443945, IIP-0726109, CNS-0708344, IIP-0934327]
- Minnesota Department of Transportation
- ITS Institute at the University of Minnesota
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [0934327] Funding Source: National Science Foundation
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.
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