Temporal Segmentation of Fine-grained Semantic Action: A Motion-Centered Figure Skating Dataset

Shenglan Liu, aibin zhang, Yunheng Li, jian zhou, Li Xu

Abstract:

Temporal Action Segmentation (TAS) has achieved great suc-
cess in many fields such as exercise rehabilitation, movie
editing, etc. Currently, task-driven TAS is a central topic
in human action analysis. However, motion-centered TAS,
as an important topic, is little researched due to unavail-
able datasets. In order to explore more models and prac-
tical applications of motion-centered TAS, we introduce a
Motion-Centered Figure Skating (MCFS) dataset in this pa-
per. Compared with existing temporal action segmentation
datasets, the MCFS dataset is fine-grained semantic, special-
ized and motion-centered. Besides, RGB-based and Skeleton-
based features are provided in the MCFS dataset. Experi-
mental results show that existing state-of-the-art methods are
difficult to achieve excellent segmentation results (includ-
ing accuracy, edit and F1 score) in the MCFS dataset. This
indicates that MCFS is a challenging dataset for motion-
centered TAS. The latest dataset can be downloaded at
https://shenglanliu.github.io/mcfs-dataset/.