Haar classifiers for multiple poses
I'm trying to perform image recognition of large birds, and since the camera is moving, my usual tactic of background removal and shape recognition won't be effective. Since all adult birds of the species are remarkably similar in coloration, I was thinking that Haar classifiers may be effective, and found some resources to try and train my own classifier.
Negative images should be significantly larger than the positive images, and present similar environments but not contain the positive image. However, I haven't been able to find too many details on what makes for a good positive training image. I found some references to trying to keep a similar aspect ratio in all positive training images, but for something like a bird that can change dramatically in different poses (wings open, wings closed), is it crucial? Is it better to train multiple classifiers for each pose and orientation of the target to be recognized? Is it better to instead try to identify a subset of the object that is very consistent (like the bird's very distinctive black and white head)?
What are the considerations made when designing the positive image set for a classifier?