Trajectory Management and Analysis : Différence entre versions
De Transport
| Ligne 4 : | Ligne 4 : | ||
* Application data: | * Application data: | ||
** GPS: potentially different sampling rate | ** GPS: potentially different sampling rate | ||
| − | ** | + | ** fixed point detection (eg, BT) |
| + | ** mobile id detection (BT from mobile sensors such as smartphones (Y. Malinovskiy)) | ||
** tracking from video data: potential missing points (occlusion) | ** tracking from video data: potential missing points (occlusion) | ||
* Exceptions ?? | * Exceptions ?? | ||
* Python bindings: inspiration from OpenCV? | * Python bindings: inspiration from OpenCV? | ||
| + | * Generic interface to different database engines, possibly with spatial extensions (eg PostgreSQL with PostGIS, SQLite with SpatiaLite) | ||
* Representation of clusters, motion patterns: prototypes, paths, Gaussian processes | * Representation of clusters, motion patterns: prototypes, paths, Gaussian processes | ||
* Use of Catch for tests (https://github.com/philsquared/Catch): [https://raw.github.com/philsquared/Catch/master/single_include/catch.hpp header] only | * Use of Catch for tests (https://github.com/philsquared/Catch): [https://raw.github.com/philsquared/Catch/master/single_include/catch.hpp header] only | ||
| Ligne 17 : | Ligne 19 : | ||
==Resources== | ==Resources== | ||
| + | * Microsoft Research http://research.microsoft.com/pubs/164590/KDD12-PopularRoutes.pdf | ||
* Vlachos (LCSS) http://alumni.cs.ucr.edu/~mvlachos/publications.html | * Vlachos (LCSS) http://alumni.cs.ucr.edu/~mvlachos/publications.html | ||
* http://www-ctp.di.fct.unl.pt/~fb/gisruk2011_final.pdf | * http://www-ctp.di.fct.unl.pt/~fb/gisruk2011_final.pdf | ||
* http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5337231 | * http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5337231 | ||
* http://isl.cs.unipi.gr/pubs/theses/Frentzos_PhD_Thesis_EN.pdf | * http://isl.cs.unipi.gr/pubs/theses/Frentzos_PhD_Thesis_EN.pdf | ||
Version du 20 décembre 2012 à 17:37
- Data structure for trajectories?
- dimention: 2D, 3D?
- time dimension: constant sampling rate?
- Application data:
- GPS: potentially different sampling rate
- fixed point detection (eg, BT)
- mobile id detection (BT from mobile sensors such as smartphones (Y. Malinovskiy))
- tracking from video data: potential missing points (occlusion)
- Exceptions ??
- Python bindings: inspiration from OpenCV?
- Generic interface to different database engines, possibly with spatial extensions (eg PostgreSQL with PostGIS, SQLite with SpatiaLite)
- Representation of clusters, motion patterns: prototypes, paths, Gaussian processes
- Use of Catch for tests (https://github.com/philsquared/Catch): header only
Data Sources
- http://www.rtreeportal.org/
- http://crawdad.cs.dartmouth.edu/data.php, eg http://crawdad.cs.dartmouth.edu/epfl/mobility
- http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/default.aspx
Resources
- Microsoft Research http://research.microsoft.com/pubs/164590/KDD12-PopularRoutes.pdf
- Vlachos (LCSS) http://alumni.cs.ucr.edu/~mvlachos/publications.html
- http://www-ctp.di.fct.unl.pt/~fb/gisruk2011_final.pdf
- http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5337231
- http://isl.cs.unipi.gr/pubs/theses/Frentzos_PhD_Thesis_EN.pdf
