Comments (2)
Where did u find the 1 ?
The 36 landmarks-based features are then used to train a support vector regressor (SVR) which directly estimates a gaze directionin 3D, (θ,ϕ) representing eyeball pitch and yaw respectively. The SVR can be trained to be person-independent with a large number of images from different people, or from a small set of person-specific images for personalized gaze estimation.#
- We found that employing strong data augmentation during training improves the performance of the model in the context of gaze estimation.
UnityEyes is too perfect for real-world-scenarios
- idk but i'll also appreciate it if somebody can answer it :D
- i don't understand ur q, sry
I hope that'd help u with ur project
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@abnerdesigner1992 are you talking about ELG or DPG paper?
I think they used the simulator to train the model entirely. The MPIIGaze training data can be downloaded using the script provided in this repo so you can check yourself how the dataset looks like and if it's applicable for your suggestion.
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Related Issues (20)
- Theta and Phi
- Train-data parameter in elg-demo file
- Video_out is not defined when using record_video for testing elg demo
- Running demo elg using CPU gives overlapping frame of original image and segmented eye image instead of segmented eye image in top-left corner
- Could you share code to test on different data sets?
- can_use_eye, can_use_eyelid and can_use_iris in elg_demo.py are False HOT 1
- Stuck with --from_video option HOT 3
- Could you please tell me where the pre-training weight should be placed in the Windows system?
- The project only has elg demo. how to run dpg demo
- RuntimeError: Error deserializing object of type int64 while deserializing a floating point number.
- Some questions about the 'unityeyes.py'
- iris_centre is not mean of iris_landmarks?
- About train/test data in MPIIGaze.h5
- Collaboration offer to help progress
- Gaze Vector Accuracy HOT 1
- elgo_demo.py stucks after video is closed HOT 1
- python and tensorflow version
- [TUTORIAL] How to save ELG model to .onnx and further to TensorRT .engine HOT 1
- Long proc time when running elg_demo.py on CPU HOT 1
- Can not get pre-trained weights
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