Action Assessment by Joint Relation Graphs

Abstract

We present a new model to assess the performance of actions from videos, through graph-based joint relation modelling. Previous works mainly focused on the whole scene including the performer's body and background, yet they ignored the detailed joint interactions. This is insufficient for fine-grained, accurate action assessment, because the action quality of each joint is dependent of its neighbouring joints. Therefore, we propose to learn the detailed joint motion based on the joint relations. We build trainable Joint Relation Graphs, and analyze joint motion on them. We propose two novel modules, the Joint Commonality Module and the Joint Difference Module, for joint motion learning. The Joint Commonality Module models the general motion for certain body parts, and the Joint Difference Module models the motion differences within body parts. We evaluate our method on six public Olympic actions for performance assessment. Our method outperforms previous approaches (+0.0912) and the whole-scene analysis (+0.0623) in the Spearman's Rank Correlation. We also demonstrate our model's ability to interpret the action assessment process.