In this paper, we proposed a hierarchical clustering framework to classify vehicle motion trajectories in real traffic video based on their pairwise similarities. First raw trajectories are pre-processed and resampled at equal space intervals. Then spectral clustering is used to group trajectories with similar spatial patterns. Dominant paths and lanes can be distinguished as a result of two-layer hierarchical clustering. Detection of novel trajectories is also possible based on the clustering results. Experimental results demonstrate the superior performance of spectral clustering compared with conventional fuzzy K-means clustering and some results of anomaly detection are presented.
Similarity based vehicle trajectory clustering and anomaly detection
01.01.2005
272432 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Similarity Based Vehicle Trajectory Clustering and Anomaly Detection
British Library Conference Proceedings | 2005
|Trajectory anomaly detection system and online trajectory anomaly detection method
Europäisches Patentamt | 2024
|Grid-Based Anomaly Detection of Freight Vehicle Trajectory considering Local Temporal Window
DOAJ | 2021
|