BIGC-Net: A Body Inter-intra-parts Graph Convolutional Network for repetitive action counting

Abstract

With the continuous development of human pose estimation techniques, researchers have gradually applied them to the field of repetitive action counting, resulting in pose-level methods. However, the current researches on the pose-level are still limited. Therefore, this paper proposes a simple but efficient Body Inter-intra-parts Graph Convolutional Network (BIGC-Net). Specifically, two core modules are developed in BIGC-Net: the Global Inter-Part Feature Learning Module (GIFL-Module) and the Salient Intra-Part Feature Learning Module (SIFL-Module). Unlike previous pose-level methods, which only model human joints globally and ignore local details. Instead, we innovatively introduce the concept of body parts with Graph Convolutional Networks (GCN) to the repetitive action counting task. Based on the natural topology of the human body, we divide the joints into multiple inter-intra-parts, each of which is regarded as a subgraph to form the overall graph structure. The complete action is then achieved by the collaborative operation between different subgraphs, thus modelling the action execution process more accurately. Therefore, the GIFL-Module is designed to capture the global collaborative relationships between the subgraphs. However, since the body joints are segmented into multiple parts, this segmentation may ignore the variation of local detail information within the subgraphs. To address this issue, the SIFL-Module aims to capture the local interdependencies between joints within the subgraphs, and the ability to focus on the most salient features of the subgraphs as it moves. The collaboration of these two modules further enhances the feature representation capability. Finally, extensive experimental results on the challenging benchmark datasets (RepCount-pose, UCFRep-pose, and Countix-Fitness-pose) show that the proposed BIGC-Net achieves excellent performance.