A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls
| Autor: | Rahim Ali Abbaspour, Christophe Claramunt, Amin Hosseinpoor Milaghardan |
|---|---|
| Přispěvatelé: | School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran (SSGE), Institut de Recherche de l'Ecole Navale (IRENAV), Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Convex hull
Computer science Geography Planning and Development urban trajectory 0211 other engineering and technologies lcsh:G1-922 02 engineering and technology Curvature Critical point (mathematics) Open research 020204 information systems Hull 0202 electrical engineering electronic engineering information engineering Earth and Planetary Sciences (miscellaneous) [INFO]Computer Science [cs] Computers in Earth Sciences Geometric framework 021101 geological & geomatics engineering self-intersection Data manipulation language Regular polygon Informatique turning point curvature area Algorithm convex hull lcsh:Geography (General) |
| Zdroj: | ISPRS International Journal of Geo-Information ISPRS International Journal of Geo-Information, MDPI, 2018, 7 (1), pp.14 ISPRS International Journal of Geo-Information; Volume 7; Issue 1; Pages: 14 ISPRS International Journal of Geo-Information, Vol 7, Iss 1, p 14 (2018) |
| ISSN: | 2220-9964 |
| Popis: | International audience; Large volumes of trajectory-based data require development of appropriate data manipulation mechanisms that will offer efficient computational solutions. In particular, identification of meaningful geometric points of such trajectories is still an open research issue. Detection of these critical points implies to identify self-intersecting, turning and curvature points so that specific geometric characteristics that are worth identifying could be denoted. This research introduces an approach called Trajectory Critical Point detection using Convex Hull (TCP-CH) to identify a minimum number of critical points. The results can be applied to large trajectory data sets in order to reduce storage costs and complexity for further data mining and analysis. The main principles of the TCP-CH algorithm include computing: convex areas, convex hull curvatures, turning points, and intersecting points. The experimental validation applied to Geolife trajectory dataset reveals that the proposed framework can identify most of intersecting points in reasonable computing time. Finally, comparison of the proposed algorithm with other methods, such as turning function shows that our approach performs relatively well when considering the overall detection quality and computing time. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|