Learning Local Part Motion Representation for Skeleton-based Action Recognition

Abstract

Skeleton-based action human recognition has drawn increasing attentions due to its properties of robustness and conciseness, while studies in recently years mostly have focused on extracting global motion features of skeleton but ignored the correlation among joints of local parts of skeleton. In this paper, we proposed a multi-stream network model based on local part joints motion features, our model focus on features extraction of local part joint motion and effect of fusion method on action recognition, utilizing LSTM and CNN structure a new network unit to grasp spatio-temporal information of joints in skeleton sequences. In order to explore distinctive motion modality of skeletal part, multi-stream mode is adopted and conducting effective recognition with weighted-score fusion. We evaluated our method on the NTU-RGB+D dataset, our result demonstrate a comparable performance of the proposed model in human action recognition.