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by starky 2462 days ago
Well, they are having to align some number of sensors ensuring sufficient overlap to ensure that they can process the images together. This means they either are warping, aligning, and combining the image into one frame to do processing (computationally intensive), or they have significantly more overlap to ensure that one of the frames contains the entire face (storage intensive), or they are doing some super-resolution processing trickery with lower resolution sensors.

500MP x 10 ips is 5 billion pixels per second that they have to process, that is equivalent to processing 20 4K30 streams at once, even without taking into consideration the extra data you would need, then you actually need to store the data somewhere and do the facial recognition. How many of these do you think would be reasonable to have in an area?
1 comments
bArray 2462 days ago
> Well, they are having to align some number of sensors

> ensuring sufficient overlap to ensure that they can

> process the images together.

It would be much easier to avoid the issue of stitching altogether and simply process the images separately, then merge the resulting output data. As far as I'm aware, you're not going to find a GPU that can process a 500MP image efficiently.

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starky 2462 days ago
But when the images are processed separately, your required actual recorded resolution could go up quickly as your overlap between imagers needs to ensure that a face to be identified is contained within a single imager, or at least a large enough portion of the face is contained within an imager to give a sufficiently high confidence of being correct. So as I mentioned, this becomes even more storage intensive, though because there is less image processing, it becomes more CPU efficient.
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bArray 2462 days ago
I disagree, if you miss an individual on the first capture, there's always the second capture. The number of people you fail to detect because their face is halfway between two screens would likely be far exceeded by the number of faces you fail to detect because there's an issue within the algorithm itself or simply the face isn't fully visible.

That's okay. There are of course limitations. You can't detect faces you can't see, for example (i.e. somebody walking the wrong direction). If you're detecting 10k faces at an accuracy of 99.99%, one in every detection frame is a failure.

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