[slingnow]

It seems like your expectations must come from frontend development or something, where you can hot glue together a few packages to instantly get results. It is absolutely possible to do what you want, but it still would take a little knowledge / engineering to make it work.

[atoav]

No, I am just exploring the possibilities. I thought getting multiple persons positions from a live video stream was a sort of bread and butter problem of computer vision and my (even in hindsight not unreasonable) expectation was that there would probably be enough existing solutions out there.

Turns out my expectations were wrong, or I have been at least fooled by the marketing material.

This is a "(broke) artist friend asked me for an art installation"-scenario, so if I don't have to I'd like to not reinvent the wheel and invest my time where it makes the biggest impact.

[r2_pilot]

I use DepthAI cameras for a bunch of CV/ML stuff [https://shop.luxonis.com/] (the models run on-camera the most part) with a Jetson Nano Orin as the microcontroller. I used to use just the Jetson Nano but Nvidia is trying hard to get people on the new Orin, so I finally paid the Nvidia tax and developing for it became exciting again. But in your case, any raspberry pi would work since the detection events are not processed on the raspberry pi and the camera processor is relatively beefy. Check out their gen 2 examples for some of the models related to your task. Although looking at crowd counting example [https://github.com/luxonis/depthai-experiments] it may not run fast enough for your needs. But if you had the processing power freed up on the raspberry pi, possibly you could just display the 30fps frames and loosely sync the person bounding box detection once a second or so, which would look sorta normal for anyone watching. Anyway, sorry to ramble, I've just been working on those code samples a lot lately and I think this may be helpful for you or someone else in this problem space.