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Review and Perspective for Distance Based Trajectory Clustering

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arxiv 1508.04904 v1 pith:IIHTWRYT submitted 2015-08-20 stat.ML cs.LGstat.AP

Philippe Besse (INSA Toulouse , IMT) , Brendan Guillouet (IMT) , Jean-Michel Loubes , Royer François This is my paper · ORCID
classification stat.MLcs.LGstat.AP
keywords clusteringdistancecomparemethodsobservationsreviewtrajectoriesaccording
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In this paper we tackle the issue of clustering trajectories of geolocalized observations. Using clustering technics based on the choice of a distance between the observations, we first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then based on the limitations of these methods, we introduce a new distance : Symmetrized Segment-Path Distance (SSPD). We finally compare this new distance to the others according to their corresponding clustering results obtained using both hierarchical clustering and affinity propagation methods.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Crowd4D: Scene-Aware Monocular 4D Crowd Reconstruction

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Crowd4D introduces HSIP scene-anchored proxies and structural coherence regularization to reconstruct scene-consistent 4D crowds from monocular video, outperforming DyCrowd on VirtualCrowd.

  2. RED: Effective Trajectory Representation Learning with Comprehensive Information

    cs.LG 2024-11 conditional novelty 5.0 of 10

    RED is a masked-autoencoder trajectory representation learner that preserves key road segments during masking and encodes spatial, temporal, and user information together, improving downstream task accuracy on three datasets.

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