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Comment by newfeatureok | Hacker News Reader

I'm somewhat surprised at the responses for this.

I believe your issue can be easily solved - have supervisors wear a distinctive color from a non-supervisor. For example let's say it's yellow.

OK so now you have yellow wearing supervisors and everyone else. To resolve the issue you have described acquire a month or so of footage, with labels per minute describing how many yellow wearing supervisors and how many people (in total) there are.

So the data you have is:

1. Yellow wearing supervisors

2. Total amount of workers on the floor

Then with this data you can train a network to do what you're describing pretty easily. Assuming there are a lot of workers on the floor, trying to do person detection or face detection would require too much data. Just have a uniform enforced and train on the colors/presence.

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This is a pragmatic and valid approach. No matter what anyone else says.

Imagine, you told a 10 YO child to do this task. Even the child would ask the same question - how do I know who is a supervisor and who is not.

Not only is face recognition hard, it is almost impossible to accomplish in a factory floor like setting. Not totally impossible but it is really really hard. Face detection is still possible but face recognition is far more computationally expensive. You'll need a shit ton of data and you'll need access to the employee database. You'll need a whole new engineering pipeline to make this happen and of course a team.

Compared to that expense and time, you are way better off getting the company to approve special vests for supes.

I remember hearing a similar story circa 2003-2008. Some BigCo was spending a bunch of money to automate their inter office mail and was hung up on OCR of handwritten stuff. Some consultants come in to look and one asks if they can just use different color envelopes/baskets. The answer was “yes”.

Sorry but it was a scenario I imagined and not something that happened in reality. I can't talk about some of the real-world scenarios that I am asked to consult on, so I made up a rather poorly thought-out one.

Something to look at is the classic image processing algorithms that can be effective and more importantly behave predictably.

In your example, take a film of the factory floor when it is empty, then once work begins use a approximately human sized/shaped rectangular sliding window and look for areas that exceed a threshold of difference to the image of the empty floor.

You can then use that window as input to a classifier which will be easier due to the considerable dimension reduction or perhaps you can get sufficient performance using further deterministic techniques.

Interesting approach, is this documented in a blog post or tutorial somewhere where something similar is done?

"Easily solved - just have them wear special clothes." Everything is easy if you can arbitrarily change the requirements!

This is good problem-solving. Why spend tens (if not hundreds) of thousands of dollars building technology to do a complicated task if you can cut that effort in half or more by having somebody where a funny vest?

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."

This particular problem is "I need to know when I don't have two managers on the floor, and they aren't always wearing funny vests just because the computer guys are bad at deep learning".

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.

I don't think they are making up arbitrary rules, I think it's problem solving. Brainstorming alternative solutions that are cost effective and solve the problem is a useful exercise. We shouldn't just blindly use machine learning because it's there.

Honestly, despite your facetiousness, this is the best starting point. And then from here work up to more complex solutions if there are reasons why rhis simple one isn’t suitable

This wouldn't work as there is a time requirement of 20 minutes. A solution to this would have to be real-time and not require one to manually log their presence, which would defeat the whole point.

The problem with supervisors was just an example. The person asking the question isn't served by simplifying the problem, because clearly they are after a more general solution.

The requirements were not changed. Supervisors of almost every working class position already wear different clothes to begin with. Heck, even doctors wear different clothing than nurses, teachers than students, coaches from athletes, etc.

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.

This solution would change the work place culture - and I 100% bet would lead to a lot of good (for MY definition of good!) supervisors leaving.

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!

I really don't understand your comment.

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.

I don't have a deep-learning solution to the problem (I know nothing about it, that's why I clicked on it!). Seems really hard to me. I'd certainly go with an obvious "clock in and out" or "rfid" approaches... but I've worked in factories - and if you make someone wear some special clothes (or do some special tasks) when they didn't have to before - you're asking for trouble. People really hate change. That's presumably why the OP was looking for an answer for a tough problem.

A nerd analogy would be making a programmer change OSs (or even text editors) against their will: They could do it, but they won't be very happy about it.

"nerd", "computer guys bad at..." it feels like you have an irrational axe to grind here, when a simple solution presented causes this line of argument.

Changing the context is one of the many well respected ways to solve a problem.

This is not bad, but once in this territory, why not just add some tracking beacon to a badge?

That is also a good idea. It really depends on what the rest of the requirements are.

Reminds me of this:

https://userweb.cs.txstate.edu/~br02/cs1428/ShortStoryForEng...

Just passing on info but ANA (the airline company) has colored helmets in their maintenance to facility to distinguish supervisors (color 1) from non supervisors (color 2) and 1st year employees (colors 3) and guests (color 4). I don't know if they do any tracking.

This example was totally made-up.

In my experience related to the type of arrangement you're describing - in reality (at least anecdotally speaking) the helmets are often not worn, or the colors are not enforced, or the colors don't get picked up due to poor quality video.

I deal mostly with third-world countries so safety standards are not always the best.

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