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Trajectory clustering via deep representation learning | IEEE Conference Publication | IEEE Xplore

Trajectory clustering via deep representation learning

Publisher: IEEE

Abstract:

Trajectory clustering, which aims at discovering groups of similar trajectories, has long been considered as a corner stone task for revealing movement patterns as well a...View more

Abstract:

Trajectory clustering, which aims at discovering groups of similar trajectories, has long been considered as a corner stone task for revealing movement patterns as well as facilitating higher-level applications like location prediction. While a plethora of trajectory clustering techniques have been proposed, they often rely on spatiotemporal similarity measures that are not space- and time-invariant. As a result, they cannot detect trajectory clusters where the within-cluster similarity occurs in different regions and time periods. In this paper, we revisit the trajectory clustering problem by learning quality low-dimensional representations of the trajectories. We first use a sliding window to extract a set of moving behavior features that capture space- and time-invariant characteristics of the trajectories. With the feature extraction module, we transform each trajectory into a feature sequence to describe object movements, and further employ a sequence to sequence autoencoder to learn fixed-length deep representations. The learnt representations robustly encode the movement characteristics of the objects and thus lead to space- and time-invariant clusters. We evaluate the proposed method on both synthetic and real data, and observe significant performance improvements over existing methods.
Date of Conference: 14-19 May 2017
Date Added to IEEE Xplore: 03 July 2017
ISBN Information:
Electronic ISSN: 2161-4407
Publisher: IEEE
Conference Location: Anchorage, AK, USA

References

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