Detail-Preserving Trajectory Summarization Based on Segmentation and Group-Based Filtering

  • Ting Wu
  • Qing XuEmail author
  • Yunhe Li
  • Yuejun GuoEmail author
  • Klaus Schoeffmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)


In this paper, aiming at preserving more details of the original trajectory data, we propose a novel trajectory summarization approach based on trajectory segmentation. The proposed approach consists of five stages. First, the proposed relative distance ratio based abnormality detection is performed to remove outliers. Second, the remaining trajectories are segmented into sub-trajectories using the minimum description length (MDL) principle. Third, the sub-trajectories are combined into groups by considering both spatial proximity, through the use of searching window, and shape restriction. And the sub-trajectories within the same group are resampled to have the same number of sample points. Fourth, a non-local filtering method based on wavelet transformation is performed on each group. Fifth, the filtered sub-trajectories which derived from the same trajectory are linked together to present the summarization result. Experiments show that our algorithm can obtain satisfactory results.


Trajectory summarization Trajectory segmentation Non-local filtering Detail-preserving 



This work has been funded by Natural Science Foundation of China under Grants Nos. 61471261 and 61771335. The author Yuejun Guo acknowledges support from Secretaria dUniversitats i Recerca del Departament dEmpresa i Coneixement de la Generalitat de Catalunya and the European Social Fund.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Graphics and Imaging LabUniversity of GironaGironaSpain
  3. 3.Klagenfurt UniversityKlagenfurtAustria

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