this post was submitted on 18 Oct 2025
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... You assume standard human anatomical proportions, infer likely age from other visual factors to dial in those proportions more accurately, use other objects in the scene that are nearby that generally have somewhat standardized measurements, like say, countertop height in a bathroom, or, the size of the tiling used in the bath splash wall, or, the common dimensions of a bathroom itself to estimate mirror to rear wall distance.
Heck, maybe even the door itself, those often follow standard dimensions and also dimensions of the sort of basic bevelling type patterns.
If you know even roughly where this person is, say, the USA, well then all that kinda shit is more or less standardized and the same in any even semi modern dwelling.
Its... not very likely that say, she has no legs beneath her knees and is actually in a home built specifically for partial/double amputees.
And you absolutely can use the phone as another relative scale, phones tend to have distinctive features you can use to narrow down which model they could be, phones have published specs that include physical dimensions.
Basically, you just construct a 3d model approximation based off of the 2d image, and fit match.
How do you think facial recognition, gait recognition algos in surveillance cams and otherwise work?
They use a bunch of mapped reference points to estimate things like distance to subject, angular size to actual size, then dial in more more detail like distsnce between eyes, torso length, waist width, limb lengths, etc, to be able to narrow down possible specific people or at least likely group characteristics.
All of these calcs of course have error margins, but more data points and more captures, cross referenced against realistic ranges for each other = less overall error.
All of this kind of stuff is called 'electro optics' when in the military context of say a missile with a visual spectrum camera being used for guidance, been around for 50+ years, generally known as 'computer vision', its used all over the place in many other kinds of common applications, theres even a whole open source version of it.
https://opencv.org/
If you do that, then it's pretty much just guessing, isn't it? You could also just look up an average height for a woman in her country and (by definition) you would have a big chance of not being too far off. And you're definitely not getting within 1% error margin with that method.
No, its not 'just guessing', its fairly accurate inference.
You can see this pic everything above her knees, and her rough age.
You can then map all measurable (in relative terms) dimensions of body features present in the image and fairly accurately infer how long the rest of her not visible legs are, then you can compare those results to other relative reference points with their own estimates.
Im not gonna walk through all of the math on this, but please be assured that every smart surveillance camera is literally sizing you up in all kinds of ways whenever they see you, been going on for over a decade now, this is literally how large network surveillance systems work.
Are they 100% perfectly accurate?
No of course not.
Are they accurate enough that governments and corps spend tens, hundreds of billions of dollars on them?
Yes.
I have zero doubts about surveillance cameras being able to estimate person's height.
Obviously, you can use statistics to get a very close approximation. What I meant is that you can't calculate her height using purely geometry because you don't see her whole body. And the 1% error margin suggests a very precise calculation, not a statistical approximation.
oooooooOoohhhh...
Sorry, I got too single minded about my own uh, more broad attempt to answer your questions.
Ok, uh, yes, I also think a +/-1% margin or error based on just one 'ruler' (the phone) and then one angle... yeah, thats probably narrow to a silly degree, mainly because ... there's no actual explanation of how they determined the phone model/size.
But maybe I'm not explaining what I mean by body proportions.
If you know her upper arm length, her lower arm length, her hand/palm size, her head size, her neck to waist length, her waist width, her chest width, shoulder width, upper leg to knee length...
You can actually map all that onto an anatomical map that matches the rest of her fitness level, and you can get a pretty darn good guess how long her leg under the knee is.
I am of course assuming something like that was done, though that was not expliclitly shown.
She could be wearing platforms, that could throw that off, and if I had to guess just by eyeballing, she may be wearing roughly 1 to 2 inch heels, as I at least think she has a bit of forward pelvic tilt going on, and that could possibly explain the kind of staggered, leaning, off center stance... but again thats basically just an educated guess from having spent a good deal of time around ladies in boots and heels, lol.
I dunno, I guess I am trying to say there is probably more to this than what the image expliclitly shows, but that could very well be just... me projecting my own way that I would try to do this, into the image.
Guess I'm trying to say there's more to it than this all being total bullshit, but also, yeah, ... hard to say precisely how accurate this presented estimate actually is or isn't.
Yeah, I get what you mean about proportions and I agree with everything you say. I just didn't do a very good job of precisely explaining my thoughts in the original comment.
Hey no problem, it seems we eventually both ended up understanding each other's ideas a bit better =P
Yes it is guessing based hard empirical data about standard proportion of human body sizes. You do know that we can accurately measure a person's size by bone lengths, such as thigh bone, forearm or arm bones.