So this is my largest-scale training run for the classical machine-vision object detector using HOG + SVMs.

Method

I used 3500 non-elephant images for the negative training set. This consisted entirely of animals (e.g. rhinos 45, sloth bears 95, Indian cow breeds 41, tigers 210, water buffalo 210, ducks, people, etc.). I used 330 elephant images for the positive training set. I removed the elephant bums! The images were all cropped to 256 width * height with aspect ratio preserved. This may have caused problems which I will examine later. I did hard-negative mining with 50 images.

Results

I've used n=50 for the false-positive testing image set. It's the same set as I used in test #4 with a selection of animals which I thought likely to be present in same environment as elephants. The false-positive result was 16%. That's much better than my previous 26%!!

I've only done n=10 for the false-negative testing image set so far. But I'm at 0% so far! Really great, especially considering I think the sliding window size was a bit off. And the hard-negative mining was lower than I'd planned.

* update:

Unfortunately it hasn't been as successful as I hoped! Once I increased increased my number of elephant images for the false-negative testing I got an awful lot of false-negatives!! It looks like this might be because the positive images have been resized badly and the sliding window size was changed to an inappropriate size!

[Images: false-positive testing set with the false-positives marked with red crosses]