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Lets get you some answers on your first attempts
Should I be getting images from all angles? Should blurry images be omitted?
Both questions can be answered as YES. For a cascade object detector you want as much variance in your training data as possible. Adding different angles can help, so does motion blur, ... this empowers the algorithm to look for features that are invariant to your position from which you take the image.
The negative images I used were also taken from a video using ffmpeg. The video is of the surrounding area, minus the sign of course.
Great! Keep in mind however that this will create a detector that will only run in that neighborhood.
... but for whatever reason I cannot get through the training for this type of object.
Can you explain this? As far as I see it, it the algorithm is just not able to get a new negative image. Can you confirm that each path in your negative.txt is actually correct? Because the problem seems to be there. Also you can check how your model is doing up till now. Retrain using -numStages 3 which will merge the previously trained stages into a model which you can use to see how you perform. Looking at your accuracy of 2.51281e-05 it might possible be already more then robust enough!
