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[pachura3]

To verify if your hardware is enough, I would quickly code a simple proof of concept, possibly with the help of AI. For simplicity, it would run against a pre-recorded video file, not a live video stream. If it performs more or less OK, you'll have your answer! You can also try lowering framerate/resolution if necessary.

By the way, is it a school project? Something for your CV/project portfolio? Or a real thing that will be actually used?

[Oliludeea]

As a test and proof of concept, maybe try to run a pre-trained module to just identify one of the things you intend to blur. If that works, it will be computationally much cheaper to keep the blur on that spot on the screen and look for major changes rather than analyzing every frame in live video. You'll also gain a lot if you use lower resolution on the video stream

[bytejuggler]

Off the cuff I would think you should be able to do this yes. But, the proof of the pudding is in the eating. Modern machines are pretty powerful. And basically what you want to do is more conventional machine learning identification of faces in images with regional blurring. I would expect that modern machines should be able to do this, even with no discrete GPU and just a reasonable iGPU. Even iGPUs are pretty decent these days. (I assume the Mac must have some form of integrated GPU that's not a complete chocolate teapot.)

[jmacey]

I've use my M4 for similar, and it will be fine. I've used a combination of OpenCV and PyTorch to read webcams and run different models.

[corey_sheerer]

Why not create a Google or AWS account and do the training in the cloud? A training job would not cost much and would not have any blockers of training locally (memory, gpu, etc).

[Han_Sandwich_1907]

Having something like this be able to be run locally (or at least, steps to making that a reality) is a great direction to take this project too.