Something to look at is the classic image processing algorithms that can be effective and more importantly behave predictably.
In your example, take a film of the factory floor when it is empty, then once work begins use a approximately human sized/shaped rectangular sliding window and look for areas that exceed a threshold of difference to the image of the empty floor.
You can then use that window as input to a classifier which will be easier due to the considerable dimension reduction or perhaps you can get sufficient performance using further deterministic techniques.
Comments
Something to look at is the classic image processing algorithms that can be effective and more importantly behave predictably.
In your example, take a film of the factory floor when it is empty, then once work begins use a approximately human sized/shaped rectangular sliding window and look for areas that exceed a threshold of difference to the image of the empty floor.
You can then use that window as input to a classifier which will be easier due to the considerable dimension reduction or perhaps you can get sufficient performance using further deterministic techniques.
Interesting approach, is this documented in a blog post or tutorial somewhere where something similar is done?