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Object detection via not-background detection?

How do you train a tensorflow model on a set of “background” or “normal” images in order to detect objects that are not “normal”?

I’m using tensorflow to detect birds at a bird feeder via images from a wyze security camera. I have had it working okay on and off over the last several weeks (3:1 false positives to actual positives is about as good as it’s gotten…), but lately it’s been really struggling, especially with wind moving the birdfeeders around (today I had almost 30,000 images, meaning it only dropped about half of all possible images run through the model…). This got me thinking that, from my point of view, it would be easiest to have a model that I continually teach what NOT to call an object and just retrain whenever I find the false positives to be a problem. THEN I can start training it to differentiate between the things that are “not-background”… I’m sure this isn’t a novel idea, so how is this done? I’ve found plenty of tutorials and articles about the opposite (training for recognition of specific objects) but can’t find anything helpful in taking this approach, at least nothing related to how to actually do this…

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