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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)

Abstract

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.

Keywords

Trajectory summarization Trajectory segmentation Non-local filtering Detail-preserving 

Notes

Acknowledgment

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.

References

  1. 1.
    Animal movements. http://www.fs.fed.us/pnw/starkey/data/tables/. Accessed 13 Apr 2018
  2. 2.
    Best track dataset. http://weather.unisys.com/hurricane/atlantic/. Accessed 13 Apr 2018
  3. 3.
    Edinburgh dataset. http://homepages.inf.ed.ac.uk/rbf/FORUMTRACKING/. Accessed 13 Apr 2018
  4. 4.
    School bus dataset. http://chorochronos.datastories.org/?q=node/6. Accessed 13 Apr 2018
  5. 5.
    Alewijnse, S., Buchin, K., Buchin, M., Kölzsch, A., Kruckenberg, H., Westenberg, M.A.: A framework for trajectory segmentation by stable criteria. In: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 351–360. ACM (2014)Google Scholar
  6. 6.
    Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. ACM SIGMOD Rec. 28, 49–60 (1999)CrossRefGoogle Scholar
  7. 7.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60–65. IEEE (2005)Google Scholar
  8. 8.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Das, R.D., Winter, S.: Automated urban travel interpretation: a bottom-up approach for trajectory segmentation. Sensors 16(11), 1962 (2016)CrossRefGoogle Scholar
  10. 10.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol. 96, pp. 226–231 (1996)Google Scholar
  11. 11.
    Gaffney, S., Smyth, P.: Trajectory clustering with mixtures of regression models. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 63–72. ACM (1999)Google Scholar
  12. 12.
    Gaffney, S.J., Robertson, A.W., Smyth, P., Camargo, S.J., Ghil, M.: Probabilistic clustering of extratropical cyclones using regression mixture models. Clim. Dyn. 29(4), 423–440 (2007)CrossRefGoogle Scholar
  13. 13.
    Guo, Y., Xu, Q., Luo, X., Wei, H., Bu, H., Sbert, M.: A group-based signal filtering approach for trajectory abstraction and restoration. Neural Comput. Appl. 29, 1–17 (2018)Google Scholar
  14. 14.
    Laurikkala, J., Juhola, M., Kentala, E., Lavrac, N., Miksch, S., Kavsek, B.: Informal identification of outliers in medical data. In: Fifth International Workshop on Intelligent Data Analysis in Medicine and Pharmacology, vol. 1, pp. 20–24 (2000)Google Scholar
  15. 15.
    Laxhammar, R., Falkman, G.: Online learning and sequential anomaly detection in trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1158–1173 (2014)CrossRefGoogle Scholar
  16. 16.
    Lee, J.G., Han, J., Whang, K.Y.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 593–604. ACM (2007)Google Scholar
  17. 17.
    Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. theory 28(2), 129–137 (1982)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Luo, X., Xu, Q., Guo, Y., Wei, H., Lv, Y.: Trajectory abstracting with group-based signal denoising. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015, Part III. LNCS, vol. 9491, pp. 452–461. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-26555-1_51CrossRefGoogle Scholar
  19. 19.
    Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. 6(3), 29 (2015)CrossRefGoogle Scholar
  20. 20.
    Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5(3), 38 (2014)Google Scholar

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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