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.
|Original language||English (US)|
|Number of pages||11|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|State||Published - Sep 1 2010|
- Clustering of trajectories
- time-series similarity measures
- unsupervised learning