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

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Temporal Action Segmentation (TAS) has achieved great success 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 unavailable datasets. In order to explore more models and practical applications of motion-centered TAS, we introduce a Motion-Centered Figure Skating (MCFS) dataset in this paper. Compared with existing temporal action segmentation datasets, the MCFS dataset is fine-grained semantic, specialized and motion-centered. Besides, RGB-based and Skeleton-based features are provided in the MCFS dataset. Experimental results show that existing state-of-the-art methods are difficult to achieve excellent segmentation results (including 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/.

Citation: Liu, S., Zhang, A., Li, Y., Zhou, J., Xu, L., Dong, Z., & Zhang, R. (2021, May). Temporal segmentation of fine-gained semantic action: A motion-centered figure skating dataset. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 3, pp. 2163-2171).