Advertisement

Movement Behaviour Recognition for Water Activities

  • Mirco Nanni
  • Roberto Trasarti
  • Fosca Giannotti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10708)

Abstract

This work describes an analysis process for the movement traces of users during water activities. The data is collected by a mobile phone app that the Navionics company developed to provide to its users sea maps and navigation services. The final objective of the project is to recognize the prevalent activity types of the users (fishing, sailing, cruising, canoeing), in order to personalize services and advertising.

References

  1. 1.
    Claramunt, C., Ray, C., Camossi, E., Jousselme, A.-L., Hadzagic, M., Andrienko, G.L., Andrienko, N.V., Theodoridis, Y., Vouros, G.A., Salmon, L.: Maritime data integration and analysis: recent progress and research challenges. In: Proceedings of the 20th International Conference on Extending Database Technology, EDBT 2017, Venice, Italy, 21–24 March 2017, pp. 192–197 (2017)Google Scholar
  2. 2.
    Napoli, A., Gallen, R., Bouju, A., Ray, C., Iphar, C.: DeAIS project: detection of AIS spoofing and resulting risksGoogle Scholar
  3. 3.
    de Souza, E.N., Boerder, K., Matwin, S., Worm, B.: Improving fishing pattern detection from satellite AIS using data mining and machine learning. Plos One 11(7), 1–20 (2016)Google Scholar
  4. 4.
    Jin, X., Han, J.: K-means clustering. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 563–564. Springer, Boston (2010).  https://doi.org/10.1007/978-0-387-30164-8_425 Google Scholar
  5. 5.
    Liao, L., Fox, D., Kautz, H.: Extracting places and activities from GPS traces using hierarchical conditional random fields. Int. J. Robot. Res. 26(1), 119–134 (2007)CrossRefGoogle Scholar
  6. 6.
    Rinzivillo, S., Gabrielli, L., Nanni, M., Pappalardo, L., Pedreschi, D., Giannotti, F.: The purpose of motion: learning activities from individual mobility networks. In: International Conference on Data Science and Advanced Analytics, DSAA 2014, Shanghai, China, 30 October–1 November 2014, pp. 312–318 (2014)Google Scholar
  7. 7.
    Ronao, C.A., Cho, S.-B.: Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst. Appl. 59, 235–244 (2016)CrossRefGoogle Scholar
  8. 8.
    Zhu, F.: Abnormal vessel trajectories detection in a port area based on AIS data. In: ICTE 2015, pp. 2043–2049 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mirco Nanni
    • 1
  • Roberto Trasarti
    • 1
  • Fosca Giannotti
    • 1
  1. 1.ISTI CNR - KDD LabPisaItaly

Personalised recommendations