Improve pose estimation for small-scale persons using head and body centers with Cheng

Improve pose estimation for small-scale persons using head and body centers with Cheng

Bottom-Up 2D Pose Estimation via Dual Anatomical Centers for Small-Scale Persons
arXiv paper abstract https://arxiv.org/abs/2208.11975v1
arXiv PDF paper https://arxiv.org/pdf/2208.11975v1.pdf

In multi-person 2D pose estimation, the bottom-up methods simultaneously predict poses for all persons, and unlike the top-down methods, do not rely on human detection.

However, the SOTA bottom-up methods’ accuracy is still inferior compared to the existing top-down methods.

… propose multi-scale training to enhance the network to handle scale variation with single-scale testing, particularly for small-scale persons.

… introduce dual anatomical centers (i.e., head and body) … can predict the human poses more accurately and reliably, especially for small-scale persons.

… method achieves 38.4% improvement on bounding box precision and 39.1% improvement on bounding box recall over the state of the art (SOTA) on the challenging small-scale persons subset of COCO.

For the human pose AP evaluation, … achieve a new SOTA (71.0 AP) on the COCO test-dev set with the single-scale testing …

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I apply innovative technologies like machine learning, computer vision, and physics to further an organization's goals. Am recognized innovator with 66 patents.