Hi, this is the result so far from my larger-scale training run (see here). Unfortunately, it wasn't quite as large-scale as I hoped due to problems with the color-spaces and sizes of images from the caltech-256 dataset.

The training run entailed the usage of:

• 0.5*350 (positive) elephant images that I'd obtained and cropped manually
• 2000 (negative) non-elephant images from caltech dataset. These are mostly of animals
• Hard-negative mining on 50 negative images

For the elephant images I included front-view, side-view at various angles, rear view (elephant bum), and close-up of elephant faces (range <1m).

Workflow using m4.4xlarge EC2 instance:

• Extract features (time = 15 minutes)
• Train model (time = 120 minutes)
• Hard-negative mining (time = 120 minutes)
• Train model adding hard-negatives (time = 120 minutes)
• Download model (time = 60 minutes)

Approx cost using m4.4xlarge EC instance: $7

Results

So far I've only had time to test the object detector on testing sets of 10 elephant images and 10 non-elephant animal images (animals likely to be present in area: sloth bears, wild pigs, cows, water buffalo, tigers, deer, humans). I was quite pleased with the results really tho! I got 0% false-negatives (i.e. failures to detect elephants when elephants present), and 20% false-positives (i.e. detect elephants when elephants not present).

Update!

So on a testing set of 50 non-elephants I got a 26% false-positive!

Interestingly, the false-positives occurred with animals having bums which looked like elephant bums! The water buffalo, which really did look like an elephant bum even to me! And the cow, which looked a bit like one. However, the color was wrong. But this object detector is using grayscale not BGR.

[Image: false-positive with a water buffalo]

[Images: full range of images using for testing set]

Next steps:

• Removal of elephant bums (rear-view) from the training set of positive images. Since elephants are going to trigger PIR when they approach rather than walk away!
• Removal of close-up elephant faces (<1m range). Since we should have detected them way before they get this close to the camera! If they are this close they are probably going to do something nasty to the camera!!
• Fix the problems with the negative images that caused issues. I'll have to look through these manually and see what exactly the problems are! Very tedious I expect!
• So increase negative images up to 4000, and use the full 350 positive images