-
Notifications
You must be signed in to change notification settings - Fork 275
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Determining self occluded joints #328
Comments
Hello, |
@stubbb Yes I've seen the PR but this version implements inference for Python only atm, I'm using C++ API. |
@korejan As far as I have figured, feature inference and pose estimation are logically separated in the C++ API. In fact you can choose the kind of pose estimation you want to employ - currently paf or pose proposal. I never had a reason to see what is passed between the infer and paf but I am guessing checking that out would likely help your development. |
Hi, is there a reliable method to determine self occluded joints? I'm converting 2D joints to 3D via an RGB-D camera, there are cases when pose inference (correctly) predicts a joint that is self-occluded but of-course there are no depth values for an the occluded joint so I'm going to apply some hueristics to estimate the depth (such as extrapolating on previous frames).
In order to do this I need a reliable method to know when the inferred joint is an (self) occluded one. I did some tests to check the confidence scores that hyperpose outputs and it seems like I can use this because the scores are typically < 0.6 but there are cases when the score is below 0.6, joint is correctly predicated but not occluded. I guess I will need to use some extra rules such as the orientation of the person in relation to the camera. Anyway I just wanted to get your thoughts on this, maybe there's a better way I can know this from hyperpose?
The text was updated successfully, but these errors were encountered: