pose estimation accuracy about hub HOT 2 OPEN

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from hub.

Hello! Thanks for reaching out with your issue on pose estimation accuracy during dynamic motions like a baseball pitch. 🚀

It sounds like the model might be struggling with occlusions and fast movements typical in sports actions. Here are a couple of suggestions that might help improve the accuracy:

1. Data Augmentation: If not already done, consider augmenting your training dataset with more examples of similar poses and motions, especially where limbs overlap or move rapidly.

2. Model Fine-tuning: If possible, fine-tune the model on a dataset that includes more sports actions, particularly baseball pitching, to help the model better learn these specific movements.

3. Post-processing: Since you've tried interpolation, consider also implementing more advanced smoothing techniques that can handle sudden changes in pose estimation, like Kalman filters or moving average filters.

4. Increase Model Complexity: If computational resources allow, using a more complex model might capture dynamics better.

Here's a quick example of how you might implement a simple moving average for smoothing:

import numpy as np

def smooth_pose(predictions, window_size=3):
    return np.convolve(predictions, np.ones(window_size)/window_size, mode='valid')

# Example usage with dummy data
predicted_poses = np.array([10, 12, 15, 20, 28, 18, 15, 14, 13, 12])
smoothed_poses = smooth_pose(predicted_poses)
print(smoothed_poses)

Hope this helps! Let us know how it goes or if you have further questions.

from hub.