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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: enter image description here

Technician absent: enter image description here

Technician not working: enter image description here

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?

Thanks in advance!

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

Given an image, the goal is to draw the bounding box or any other geometric shape around the object of interest. In other words, you would like to localize and identify the object.

enter image description here

I recommend reading this link https://machinelearningmastery.com/object-recognition-with-deep-learning/.

Good repository for detection - https://github.com/ultralytics/yolov5.

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