Optimizing Hand Region Detection in MediaPipe Holistic Full-Body Pose Estimation to Improve Accuracy and Avoid Downstream Errors
<|reference_start|>Optimizing Hand Region Detection in MediaPipe Holistic Full-Body Pose Estimation to Improve Accuracy and Avoid Downstream Errors: This paper addresses a critical flaw in MediaPipe Holistic's hand Region of Interest (ROI) prediction, which struggles with non-ideal hand orientations, affecting sign language recognition accuracy. We propose a data-driven approach to enhance ROI estimation, leveraging an enriched feature set including additional hand keypoints and the z-dimension. Our results demonstrate better estimates, with higher Intersection-over-Union compared to the current method. Our code and optimizations are available at https://github.com/sign-language-processing/mediapipe-hand-crop-fix.<|reference_end|>
@article{moryossef2024optimizing,
    title={Optimizing Hand Region Detection in MediaPipe Holistic Full-Body Pose
  Estimation to Improve Accuracy and Avoid Downstream Errors},
    author={Amit Moryossef},
    journal={arXiv preprint arXiv:2405.03545},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03545},
    primaryClass={cs.CV}
}