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An algorithm to identify avoidance behavior in moving object trajectories

Journal of the Brazilian Computer Society volume 17, pages 193–203 (2011)Cite this article

Abstract

Research on trajectory behavior has increased significantly in the last few years. The focus has been on the search for patterns considering the movement of the moving object in space and time, essentially looking for similar geometric properties and dense regions. This paper proposes an algorithm to detect a new kind of behavior pattern that identifies when a moving object is avoiding specific spatial regions, such as security cameras. This behavior pattern is called avoidance. The algorithm was evaluated with real trajectory data and achieved very good results.

References

  1. 1.

    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Bocca JB, Jarke M, Zaniolo C (eds) VLDB. Morgan Kaufmann, San Mateo, pp 487–499

    Google Scholar 

  2. 2.

    Alvares LO, Bogorny V, Kuijpers B, de Macedo JAF, Moelans B, Vaisman A (2007) A model for enriching trajectories with semantic geographical information. In: ACM-GIS. ACM Press, New York, pp 162–169

    Google Scholar 

  3. 3.

    Andersson M, Gudmundsson J, Laube P, Wolle T (2008) Reporting leaders and followers among trajectories of moving point objects. GeoInformatica 12:497–528. doi:10.1007/s10707-007-0037-9

    Article  Google Scholar 

  4. 4.

    Baglioni M, de Macêdo JAF, Renso C, Trasarti R, Wachowicz M (2009) Towards semantic interpretation of movement behavior. In: Sester M, Bernard L, Paelke V (eds) AGILE conference, lecture notes in geoinformation and cartography. Springer, Berlin, pp 271–288

    Google Scholar 

  5. 5.

    Bogorny V, Kuijpers B, Alvares LO (2009) ST-DMQL: a semantic trajectory data mining query language. Int J Geogr Inf Sci 23:1245–1276

    Article  Google Scholar 

  6. 6.

    Cao H, Mamoulis N, Cheung DW (2006) Discovery of collocation episodes in spatiotemporal data. In: ICDM. IEEE Comput Soc, Los Alamitos, pp 823–827

    Google Scholar 

  7. 7.

    Dodge S, Weibel R, Lautenschutz A (2008) Towards a taxonomy of movement patterns. Inf Vis 8:240–252

    Article  Google Scholar 

  8. 8.

    Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: Berkhin P, Caruana R, Wu X (eds) KDD. ACM Press, New York, pp 330–339

    Google Scholar 

  9. 9.

    Gudmundsson J, van Kreveld MJ (2006) Computing longest duration flocks in trajectory data. In: de By RA, Nittel S (eds) GIS. ACM Press, New York, pp 35–42

    Google Scholar 

  10. 10.

    Hägerstrand T (1970) What about people in regional science?. Pap Reg Sci 24(1):6–21

    Article  Google Scholar 

  11. 11.

    Kim DJ, Park KH, Bien Z (2007) Hierarchical longitudinal controller for rear-end collision avoidance. IEEE Trans Ind Electron 54:805–817

    Article  Google Scholar 

  12. 12.

    Laube P, Imfeld S, Weibel R (2005) Discovering relative motion patterns in groups of moving point objects. Int J Geogr Inf Sci 19(6):639–668

    Article  Google Scholar 

  13. 13.

    Laube P, van Kreveld M, Imfeld S (2005) Finding REMO: detecting relative motion patterns in geospatial lifelines. Springer, Berlin

    Google Scholar 

  14. 14.

    Lee SW, Lee BH, Lee KD (1999) A configuration space approach to collision avoidance of a two-robot system. Robotica 17:131–141

    Article  Google Scholar 

  15. 15.

    Liu YH, Shi CJ (2005) A fuzzy-neural inference network for ship collision avoidance. In: Proceedings of 2005 international conference on machine learning and cybernetics. IEEE Comput Soc, Los Alamitos, pp 4754–4754

    Google Scholar 

  16. 16.

    Nedevschi S, Bota S, Tomiuc C (2009) Stereo-based pedestrian detection for collision-avoidance applications. Trans Intell Transp Syst 10:380–391

    Article  Google Scholar 

  17. 17.

    Ong R, Wachowicz M, Nanni M, Renso C (2010) From pattern discovery to pattern interpretation in movement data. In: Fan W, Hsu W, Webb GI, Liu B, Zhang C, Gunopulos D, Wu X (eds) ICDM workshops. IEEE Comput Soc, Los Alamitos, pp 527–534

    Google Scholar 

  18. 18.

    Palma AT, Bogorny V, Alvares LO (2008) A clustering-based approach for discovering interesting places in trajectories. In: ACMSAC. ACM Press, New York, pp 863–868

    Google Scholar 

  19. 19.

    Rocha JAMR, Times VC, Oliveira G, Alvares LO, Bogorny V (2010) DB-SMOT: a direction-based spatio-temporal clustering method. In: IEEE conference of intelligent systems. IEEE Press, New York, pp 114–119

    Google Scholar 

  20. 20.

    Shandy S, Valasek J (2001) Intelligent agent for aircraft collision avoidance. In: Proceedings of AIAA guidance, navigation, and control conference. American Institute of Aeronautics and Astronautics, Washington, pp 1–11

    Google Scholar 

  21. 21.

    Suh SH, Bishop AB (1988) Collision-avoidance trajectory planning using tube concept: analysis and simulation. J Robot Syst 5(6):497–525

    Article  Google Scholar 

  22. 22.

    Suh SH, Kim MS (1992) An algebraic approach to collision-avoidance trajectory planning for dual-robot systems: formulation and optimization. Robotica 10(02):173–182

    Article  Google Scholar 

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Author information

Affiliations

  1. INE/UFSC, Florianopolis, Brazil

    Luis Otavio Alvares & Vania Bogorny

  2. II/UFRGS, Porto Alegre, Brazil

    Luis Otavio Alvares & Alisson Moscato Loy

  3. KDD Lab, Pisa, Italy

    Chiara Renso

Authors
  1. Luis Otavio Alvares
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  2. Alisson Moscato Loy
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  3. Chiara Renso
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  4. Vania Bogorny
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Corresponding author

Correspondence to Luis Otavio Alvares.

Additional information

A previous version of this paper has appeared at GEOINFO 2010—The Brazilian Symposium on Geoinformatics.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Alvares, L.O., Loy, A.M., Renso, C. et al. An algorithm to identify avoidance behavior in moving object trajectories. J Braz Comput Soc 17, 193–203 (2011). https://doi.org/10.1007/s13173-011-0037-3

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Keywords

  • Trajectory behavior
  • Spatiotemporal pattern
  • Moving objects
  • Trajectory data mining
  • Avoidance behavior