pose estimation accuracy

wleisenr commented 4 months ago

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Question

We are experiencing some pose estimation accuracy issues particularly when the arm crosses past the head during a baseball pitching motion. See attached screenshots for example of the issue. Any recommendations for how to improve this? We have added some interpolation based on confidence which helps but this is so far off that it doesn't remediate the problem completely. dvs.zip

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pderrenger commented 4 months ago

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:

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.
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.
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.
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.

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