Aug 162010
Match GPS Data to Road Data
Last week I tried choosing the closest road segment instead of using the angle as the most important attribute. This gives significantly better results. However, the results still aren’t perfect… of course. The found road location jumps back and forth from one end of the road segment to the other. I’m fairly burnt out on trying to find the correct road location, so it’s time for a reevaluation again.
As I currently have no direction (aka no Thesis adviser at the moment) I’ll have to make up my own possible next steps.
- Get the road location to actually work.
- probably involves assuring movement on a road segment matches movement from the GPS, while still taking into account other things like distance to the segment, etc.
- once this is done there is an issue with the perspective transformation that causes the road lines to disappear in some cases. I have no idea how to fix this.
- use color information (this would theoretically work in the same way as edge detection currently works, only edges are defined by significant changes in color).
- the academic papers I’ve read say this does not work very well.
- shadows change colors so the first bit of research would be figuring out if colors can be “normalized” so that green areas in sunny areas can be seen as the same green as in shadowy areas.
- use curve fitting to fit two curved lines to the image.
- apparently this works well but it can’t handle intersections which is why I dislike the idea.
- requires research on how it works, since I don’t know.
- try to add to the edge detection algorithm.
- if it’s added to then it won’t be strictly edge detection anymore. I’m not sure what else to add to it.
- look through the academic papers again for other attributes for recognizing road versus non-road.
- the one about recognizing country roads probably has some ideas.
- the various techniques should probably be cataloged anyway since they may be useful when I actually have to write the paper.
