Impact of Annotating Occluded Keypoints on Pose Estimation Accuracy

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Hi everyone,

I’m working on keypoint annotation for training pose detection models, and I’m wondering about the impact of annotating occluded keypoints.

  • Is there a real benefit in explicitly marking a keypoint as occluded during annotation?
  • Does it improve the overall accuracy of predicted keypoints?
  • Does it help reduce errors such as joint swaps (incorrectly swapped joints) or outliers (incorrectly placed keypoints)?

If you have any insights, research references, or personal experiences on this topic, I’d love to hear your thoughts!

Thanks in advance for your input.

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I don’t know,:sweat_smile: so I tried searching on Hugging Chat, but it seems that there is no specific individual research, but there seems to be benefit in that work.

Annotating occluded keypoints in pose detection models can provide several benefits, based on logical reasoning and inferred best practices:

  1. Benefit of Occlusion Annotation: Marking occluded keypoints offers context about why a keypoint is not visible, which can help models handle missing data more effectively. This context is more informative than just noting a keypoint as missing.

  2. Improvement in Accuracy: Including occlusion status allows models to infer the position of hidden keypoints using visible ones, potentially enhancing prediction accuracy. This contextual information can lead to more informed and accurate predictions.

  3. Reduction in Errors: By recognizing occlusions, models may focus on visible keypoints, reducing errors such as joint swaps and outliers. This targeted approach can improve overall model performance and reliability.

In conclusion, while specific studies were not referenced, the reasoning suggests that occlusion annotation can enhance model performance, leading to more accurate and reliable pose estimation.

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