HaarTraining: Best way to minimally crop +ve images (for efficient detection)

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First of all, I am not talking about the utilities that can be used to crop positive images.

I want to know the best way to crop the object of interest so that minimal number of +ve images produce good detection results. There can be two types of objects,

1. Which can be cropped easily with a rectangle including considerable number of object features (objects roughly in rectangular/circular shapes)

2. Whose features cannot be captured easily in rectangle without keeping the background too in the cropping rectangle (objects in shapes like star maybe; like aircraft)

I have attached a sample cropped image of an aircraft. I am trying to build a cascade classifier with similarly cropped images but my training doesn't proceed beyond 4 stages (False detection rate achieved with 4000 +ves, 30000 -ves).

Please suggest:

1. Is it because of my cropping style or something else is wrong, that the learning stops at stage:4

2. I am using images from a single aircraft model at fairly close angles (extracted from a video), but still covering 180 deg view I require for detection. It it required to have +ve images only from same view angle?

3. Please use the attached cropped image and suggest what would be the best cropping style for these type of objects.

Thanks.