TaiChi: A Fine-Grained Action Recognition Dataset

Abstract

In this paper, we introduce TaiChi which is a fine-grained action dataset. It consists of unconstrained user-uploaded web videos containing camera motion and partial occlusions which pose new challenges to fine-grained action recognition compared to the existing datasets. In this dataset, 2,772 samples of 58 fine-grained action classes are manually annotated. Additionally, we provide the baseline action recognition results using the state-of-the-art Improved Dense Trajectory feature and Fisher Vector representation with an MAP (Mean Average Precision) of 51.39%.