# How to classify two very similar images using Deep Learning?

I am a newbie in Computer Vision.

I have a scenario in which I have a stationary camera in a factory. I want to detect whether the technician is working on the machine or not.

Images are like the following:

Technician working:

Technician absent:

Technician not working:

I am confused whether is it a Image classification issue or an Object Detection/Pose Detection problem.

As per my knowledge this should be a classification problem, I should take multiple images of a condition in which the machine is unattended, and a condition in which the technician is working on the machine.

I will train the model if with different individual technicians on different days with different clothes.

Now if I am in the right direction, how much images do I need to have a good accuracy?

I see there are different models on Tensorflow Hub on image classification like EfficientNet, etc. Which model/architecture will work for me?

I am sorry if I sound noobish.

I can train the model using simple classifiers' code (like Cat vs Dog), but I want the my architecture to understand that there is an area in the image which should only be checked if it is occupied or not to classify properly.

OR

Shall I cut out the middle area (where technician stands) simply using opencv. And then feed that cutout image to some classifier to detect if there is a human standing there?

What you are asking about can be treated as a classification problem, indeed. But I would treat it rather as a detection problem.