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A group-based signal filtering approach for trajectory abstraction and restoration

  • ICONIP 2015
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Abstract

Trajectory abstraction is used to summarize the large amount of information delivered by the trajectory data, and trajectory restoration is used to reconstruct lost parts of trajectories. To cope with complex trajectory data, in this paper, we propose a new strategy for abstracting and restoring trajectories from the perspective of signal processing. That is, trajectories are treated as signals that bear copious information that varies with time and space, and information filtering is exploited to concisely communicate the trajectory data. As for trajectory abstraction, the resampling of trajectory data is first introduced based on achieving the minimum Jensen–Shannon divergence of the trajectories before and after being resampled. Then, a non-local filtering approach is developed to perform wavelet transformations of similarity groups of these resampled trajectories to produce the trajectory summaries. Trajectory abstraction can not only offer multi-granularity summaries of trajectory data, but also identify outliers by utilizing a probabilistic definition of a group of trajectories and the Shannon entropy. Furthermore, to handle incomplete trajectory data for which some sample points are lost, the proposed non-local filtering idea is exploited to restore the incomplete data. Extensive experimental studies have shown that the proposed trajectory abstraction and restoration can obtain very encouraging results, in terms of both objective evaluation metrics and subjective visual effects. To the best of our knowledge, this is the first attempt to deploy the group-based signal filtering technique in the context of dealing with trajectory data. In addition, as a preprocessing step, the proposed trajectory abstraction can be employed to improve the performance of trajectory clustering.

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Acknowledgements

Yuejun Guo and Xiaoxiao Luo contributed equally to this work and share the first authorship.

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Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Yuejun Guo, Qing Xu, Xiaoxiao Luo, Hao Wei, Hongjuan Bu & Mateu Sbert

  2. Graphics and Imaging Lab, University of Girona, Girona, Spain

    Yuejun Guo & Mateu Sbert

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  1. Yuejun Guo
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  2. Qing Xu
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  3. Xiaoxiao Luo
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Correspondence to Qing Xu or Mateu Sbert.

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The authors declare that they have no conflicts of interest.

Additional information

This work has been funded by the Natural Science Foundation of China (61471261, 61179067, U1333110), and by the Grants TIN2013-47276-C6-1-R from the Spanish government and 2014-SGR-1232 from the Catalan government (Spain). The first author acknowledges the support from Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya and the European Social Fund.

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Guo, Y., Xu, Q., Luo, X. et al. A group-based signal filtering approach for trajectory abstraction and restoration. Neural Comput & Applic 29, 371–387 (2018). https://doi.org/10.1007/s00521-017-3148-8

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