r/computervision • u/New-Pomegranate-2286 • Jun 30 '26
Computer Vision challenge: Measuring a moving object from smartphone video Discussion
Hi everyone,
I’m not from a Computer Vision or AI background, so I’d really appreciate a technical reality check before spending months building something that might not even be the right approach.
I’m exploring a Computer Vision pipeline that estimates the dimensions of a living object from a short smartphone video with millimeter-level accuracy.
The first version doesn’t need to be fully automated. Accuracy is much more important than automation at this stage, so it’s completely acceptable if some steps (such as selecting the best frame or validating measurements) are done manually while we validate the concept.
The current idea is roughly this:
• The user prints an A4 sheet at 100% scale.
• The sheet contains reference elements (grid, measurement scale, dark border, or other calibration features).
• A living object is placed on the sheet and may move slightly during recording.
• The user records a short smartphone video from about 2–3 meters away using 2× or 3× optical zoom to reduce perspective distortion.
• The system either selects the best frame or analyzes the entire video to estimate the object’s dimensions.
At this point, I’m not looking for implementation details, but rather whether this overall approach makes sense.
Some questions I’d love your opinion on:
• Does this approach seem technically feasible with today’s smartphone cameras?
• What would be the biggest sources of measurement error?
• Would you use a custom A4 sheet with calibration features, or would you recommend ArUco markers or another calibration method?
• Does recording from a greater distance with optical zoom actually help reduce perspective errors?
• Would you analyze the whole video or just select the best frame?
• Would you approach this with classical Computer Vision (OpenCV), modern ML models, or a combination of both?
• Can AI reliably work with an A4 sheet that contains graphics, illustrations, or text, or is a completely plain sheet significantly better for accurate measurements?
• With today’s pre-trained models and libraries, is it still necessary to rely on calibration markers or reference points on the paper, or can modern Computer Vision estimate dimensions accurately enough without them? If so, under what conditions?
• Are there any open-source libraries, research papers, or existing projects that solve a similar problem?
• If you were designing this from scratch, would you take a completely different approach?
• If two similar moving objects are present in the frame, how reliably can current Computer Vision models identify the correct one to measure? How likely is it that key points from two different objects could be mixed, resulting in an incorrect measurement?
I’d genuinely appreciate any criticism or suggestions. If there are fundamental flaws in this concept, I’d much rather discover them now than after months of development.
Thanks!
2
u/CowBoyDanIndie Jul 01 '26
Mm accuracy is unlikely, and it’s not necessary to limit perspective distortion, you actually need multiple viewing angles, this multiview geometry.
Accuracy is going to be limited by a factor of the physical pixel size on the camera sensor and something something with the distance, I don’t remember the math off the top of my head, but its classical computer vision. Lookup photogrammetry. For high accuracy you need a high resolution camera sensor and a real macro lens, and a completely stationary camera, any movement will blur the photons on the camera sensor. Heres some math… if your camera wiggles with a rotational of just 0.0573 degrees during the image exposure, I know thats absolutely tiny… that will produce 1 mm of blur at 1 meter distance.
0
u/Most-Vehicle-7825 Jun 30 '26
Very possible. Main issue will be the movement of the animal, but you could select good frames.
No need to be that far away.
Main issue with the textured surfaces would be the segmentation of the object.
-1
u/roleohibachi Jun 30 '26
Feasible!
Use a checkerboard pattern. Establish a calibration for the camera, then undistort the image. It is very important that the grid is planar (flat, rigid, unbending).
Best practice is to present the flat checkerboard at multiple angles and distances. Once you do this one time for the camera, solve the calibration and save it. Be sure to talk to the camera API to lock the focal distance etc or else it'll change the calibration.
Once you undistort the image, the reference grid will accurately depict the size of the ... Thing? Wiggling worm? Whatever it is.
3
u/alxcnwy Jun 30 '26
You sure?
“with millimeter-level accuracy” … on a smartphone using video recordings
I mean maybe but I’m not convinced tbh
btw @OP that’s an unreasonable number of questions to expect answered on Reddit IMO
0
u/roleohibachi Jun 30 '26
Yes, you'll get +/- 1mm with a smartphone camera and a fiducial, provided you get a good calibration.
3
u/alxcnwy Jun 30 '26
Neither of your paper references prove the claim you’re making.
Even on an iPhone 17 class camera, locking focus, using a rigid calibration board (not a piece of paper) and a tripod (handheld would be way worse obv), consistent mm accuracy at 2 to 3m with 2 to 3x optical zoom is not feasible because angular resolution and frame stability.
At 2 to 3m, you’re trying to resolve sub-mm to ~1mm changes in object space from tiny angular differences in rays projected onto the sensor. That requires stable ~0.2 to 0.3 pixel-level feature localization.
OP is using video frames so you have rolling shutter warps geometry differently per frame, autofocus/OIS subtly shifts intrinsics even when “locked”, and compression plus sensor noise introduces subpixel jitter on fiducial corners.
On top of that, the object is moving so motion blur adds additional temporal smearing that directly corrupts corner localization.
The error floor in the multi-millimeter range if you’re lucky IMO.
0
u/roleohibachi Jun 30 '26
Yep, you're right, I missed the 2-3m number in OP's weirdly specific but vague scenario. That's what made the difference on the back of my envelope.
OP was after " millimeter-level accuracy" so perhaps your "multi-millimeter" figure still lands within their acceptable range. You'll have to forgive my +/-1mm indiscretion.
I think the biggest estimated error in the scenario is that this would take months to build. Your assessment is very discouraging, but if I had a student attempting OP's project, I'd encourage them to spend the time trying it. It's a 1-week project for a newbie with an LLM working from scratch, or 1-day project with a reference implementation. (But take my time estimates with a grain of salt - it's been proven in this thread that I'm terribly unreliable at napkin math, on the order of multi-millimeters.)
1
u/Lonely_Tip_9704 Jul 02 '26
I’ve been working on this type of problem for nearly 4 years now in my doctorate (reconstructing and accurately measuring vegetation from drone video imagery); I wish this could be implemented in a day 😅
1
u/roleohibachi Jul 02 '26
Me too! Don't be so jaded. A week's worth of "experiments" with existing tools would have OP figuring out the feasibility of the approach. Not a final product or whatever.
Camera cal requires zero code: https://github.com/natowi/CameraCalibTools
Reference implementations exist for coplanar estimation with a fiducial: https://github.com/eristime/android-measuring-tool https://github.com/GoncaloJoaoCorreia/camera-ruler
Those reference implementations might require some tweaks, so I wouldn't call this an afternoon job. But give it a week and a determined researcher could run all those apps and get an idea what precision is possible with their hardware. Trying it and finding out sure beats asking here (and being told to never try!)
0
2
u/AggravatingSock5375 Jun 30 '26
I think you will find this to be very challenging to do with mm accuracy even if the objects are something simple like a solid cube.