Trajectory Management and Analysis : Différence entre versions

Version du 13 août 2014 à 17:34

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)
- uncertain positions (spatial and time)
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
- motion prediction from historical data / large datasets
Use of Catch for tests (https://github.com/philsquared/Catch): header only

@@ Ligne 14 : / Ligne 14 : @@
 ** motion prediction from historical data / large datasets
 * Use of Catch for tests (https://github.com/philsquared/Catch): [https://raw.github.com/philsquared/Catch/master/single_include/catch.hpp header] only
+==Trajectory Filtering==
+* Wikipedia https://en.wikipedia.org/wiki/Numerical_differentiation https://en.wikipedia.org/wiki/Numerical_smoothing_and_differentiation https://en.wikipedia.org/wiki/Finite_difference_coefficients
+* papers: (from https://encrypted.google.com/search?hl=en&q=Savitzky-Golay%20vehicle%20trajectories)
+http://www.sciencedirect.com/science/article/pii/S0968090X1100091X https://encrypted.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0CCcQFjAB&url=http%3A%2F%2Fentpe.fr%2Ffr%2Fcontent%2Fdownload%2F3451%2F21866%2Ffile%2F09-3831.pdf&ei=FtnrU6uFN5D2yQSKu4DoCw&usg=AFQjCNGdla4RfBqPPmGR_KC8_lAUH2eE0A&sig2=PKT_UdGYDQYQfyekvvKerQ&bvm=bv.72938740,d.aWw http://www.academia.edu/6190206/Using_Drivers_Jerks_Profile_in_Computer_Vision-Based_Traffic_Safety_Evaluations._In_Transportation_Research_Board_93rd_Annual_Meeting_January_2014
 ==Data Sources==