Remember, the problem is "I need to know when I don't have two managers on the floor," not "how do I use machine learning to know when I don't have two managers on the floor."
If we can make up arbitrary rules and assumptions then just have them jot down on a piece of paper when they come and go, and if they are the last to leave then they have to send an email.
The general point is to capitalize on preexisting information than to do the "true" solution which is error prone and even a human might not have 100% accuracy at, due to the fact that in certain settings (such as this hypothetical) the perfected solution cannot be accomplished without constraints.
Imagine where you worked suddenly introduced this: "Yes, previously everyone could wear whatever they wanted - but from today, just the senior programmers must code while wearing a high-vis jacket around the office so we can track when they at their desks".
The supervisors have now changed their relationship with coworkers - signaling their superiority, while simulataneously feeling stalked by their bosses, and looking "unfashionable"/un-cool - all because someone couldn't figure out how to do deep learning properly... which was the OP was actually asking about!
1. Supervisors are already by definition "superior" than their subordinates.
2. Supervisors on factories already wear distinctive clothing - especially in fully automated factories.
Finally, you have yet to propose a solution to the problem yourself that would be highly accurate and easy to train. You vastly underestimate the difficulty to create a bespoke solution from scratch and no data.
In any case since the supervisor thing was just an example - the original poster's only real choice is to manually label everything, but AI is really problem centric so it's hard to recommend anything without knowing the actual problem. Assuming it really is just [someone in an area for a period of time] kind of problem, and the difficulty is picking apart the 'someone' and you cannot influence their behavior, you just need massive amounts of data. Even then there's no guarantee you'll have high accuracy.
If high accuracy is required the problem itself needs to be examined on a higher level.
