I am trying to use machine learning / neural networks to count how many units of an object type on front row of a shelf in a photo (let's call that object as "green beer").
As you can see in the photo below, when captured at direct angle, the green beer cans on behind row and front row are almost indistinguishable, and normal object detection neural networks can easily be confused.
Note: Beverage brand is intentionally blured.
I have an idea that if somehow I can combine photos with different angles and build a 3D space based on them, then I can count more precisely how many beer cans on front row only.
I am really really new to machine learning, so please suggest me some algorithm names or links if someone has already encountered this situation. I prefer neural networks solution but any other kinds of machine learning are also welcomed.
Thank you very much.