Comparison of marker-less 2D image-based methods for infant pose estimation
<|reference_start|>Comparison of marker-less 2D image-based methods for infant pose estimation: There are increasing efforts to automate clinical methods for early diagnosis of developmental disorders, among them the General Movement Assessment (GMA), a video-based tool to classify infant motor functioning. Optimal pose estimation is a crucial part of the automated GMA. In this study we compare the performance of available generic- and infant-pose estimators, and the choice of viewing angle for optimal recordings, i.e., conventional diagonal view used in GMA vs. top-down view. For this study, we used 4500 annotated video-frames from 75 recordings of infant spontaneous motor functions from 4 to 26 weeks. To determine which available pose estimation method and camera angle yield the best pose estimation accuracy on infants in a GMA related setting, the distance to human annotations as well as the percentage of correct key-points (PCK) were computed and compared. The results show that the best performing generic model trained on adults, ViTPose, also performs best on infants. We see no improvement from using specialized infant-pose estimators over the generic pose estimators on our own infant dataset. However, when retraining a generic model on our data, there is a significant improvement in pose estimation accuracy. The pose estimation accuracy obtained from the top-down view is significantly better than that obtained from the diagonal view, especially for the detection of the hip key-points. The results also indicate only limited generalization capabilities of infant-pose estimators to other infant datasets, which hints that one should be careful when choosing infant pose estimators and using them on infant datasets which they were not trained on. While the standard GMA method uses a diagonal view for assessment, pose estimation accuracy significantly improves using a top-down view. This suggests that a top-down view should be included in recording setups for automated GMA research.<|reference_end|>
@article{jahn2024comparison,
    title={Comparison of marker-less 2D image-based methods for infant pose
  estimation},
    author={Lennart Jahn, Sarah Fl"ugge, Dajie Zhang, Luise Poustka, Sven
  B"olte, Florentin W"org"otter, Peter B Marschik and Tomas Kulvicius},
    journal={arXiv preprint arXiv:2410.04980},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04980},
    primaryClass={cs.CV}
}