Multi-Person Full Body Pose Estimation
This work addresses a specific gap in pose estimation for multi-person scenarios, though it appears incremental as it builds on existing systems like AlphaPose.
The paper tackles multi-person full body pose estimation by developing an integrated model using knowledge distillation, achieving 51.5 mAP on a manually annotated validation dataset based on the AlphaPose system and MSCOCO2017 dataset.
Multi-person pose estimation plays an important role in many fields. Although previous works have researched a lot on different parts of human pose estimation, full body pose estimation for multi-person still needs further research. Our work has developed an integrated model through knowledge distillation which can estimate full body poses. Trained based on the AlphaPose system and MSCOCO2017 dataset, our model achieves 51.5 mAP on the validation dataset annotated manually by ourselves. Related resources are available at https://esflfei.github.io/esflfei.gethub.io/website.html.