Saturday, April 23, 2011


Trained with different SVM kernels. SVM  parameters affects the overall detection performance.
SVM also consumes much more processing time which I can do nothing to optimize so far.
The following are the results of different SVM parameter settings.
According to the reference paper, the group trained for a few days. It should be understood that usually the more training data the better.
As the deadline is approaching, it seems impossible for me to apply non-maximum suppression, instead I’ll select best performance SVM parameter combination ,run error check and get all benchmark ready.

1 comment:

  1. Nice screenshots. In addition to the best parameter combinations, I'm also interested in graphs that show how performance changes with respect to changes in different parameters. Don't go overboard with graphs though, just include the most interesting/relevant ones.