2011
Journal article
Open Access
An algorithm to identify avoidance behavior in moving object trajectories
Alvares L. O., Loy A. M., Renso C., Bogorny V.
Computer Science(all) Trajectory data mining Avoidance behavior Moving objects General Computer Science Trajectory behavior Spatiotemporal pattern
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.
Source: Journal of the Brazilian Computer Society (Impr.) 17 (2011): 193–203. doi:10.1007/s13173-011-0037-3
Publisher: Sociedade Brasileira de Computação., Rio de Janeiro, RJ, Brasile
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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
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
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
21. Suh SH, Bishop AB (1988) Collision-avoidance trajectory planning using tube concept: analysis and simulation. J Robot Syst 5(6):497-525
22. Suh SH, Kim MS (1992) An algebraic approach to collisionavoidance trajectory planning for dual-robot systems: formulation and optimization. Robotica 10(02):173-182
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
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
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
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
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
7. Dodge S, Weibel R, Lautenschutz A (2008) Towards a taxonomy of movement patterns. Inf Vis 8:240-252
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
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
10. Hägerstrand T (1970) What about people in regional science?. Pap Reg Sci 24(1):6-21
11. Kim DJ, Park KH, Bien Z (2007) Hierarchical longitudinal controller for rear-end collision avoidance. IEEE Trans Ind Electron 54:805-817
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
13. Laube P, van Kreveld M, Imfeld S (2005) Finding REMO: detecting relative motion patterns in geospatial lifelines. Springer, Berlin
14. Lee SW, Lee BH, Lee KD (1999) A configuration space approach to collision avoidance of a two-robot system. Robotica 17:131- 141
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
16. Nedevschi S, Bota S, Tomiuc C (2009) Stereo-based pedestrian detection for collision-avoidance applications. Trans Intell Transp Syst 10:380-391
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
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
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
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
21. Suh SH, Bishop AB (1988) Collision-avoidance trajectory planning using tube concept: analysis and simulation. J Robot Syst 5(6):497-525
22. Suh SH, Kim MS (1992) An algebraic approach to collisionavoidance trajectory planning for dual-robot systems: formulation and optimization. Robotica 10(02):173-182
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BibTeX entry
@article{oai:it.cnr:prodotti:199471,
title = {An algorithm to identify avoidance behavior in moving object trajectories},
author = {Alvares L. O. and Loy A. M. and Renso C. and Bogorny V.},
publisher = {Sociedade Brasileira de Computação., Rio de Janeiro, RJ, Brasile},
doi = {10.1007/s13173-011-0037-3},
journal = {Journal of the Brazilian Computer Society (Impr.)},
volume = {17},
pages = {193–203},
year = {2011}
}CNR authorsLaboratories
0000-0002-1763-2966