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:
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.
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!