Improving Small-Scale Human Action Recognition Performance Using a 3D Heatmap Volume.

ABSTRACT: In recent years, skeleton-based human action recognition has garnered significant research attention, with proposed recognition or segmentation methods typically validated on large-scale coarse-grained action datasets. However, there remains a lack of research on the recognition of small-scale fine-grained human actions using deep learning methods, which have greater practical significance. To address this gap, we propose a novel approach based on heatmap-based pseudo videos and a unified, general model applicable to all modality datasets. Leveraging anthropometric kinematics as prior information, we extract common human motion features among datasets through an ad hoc pre-trained model. To overcome joint mismatch issues, we partition the human skeleton into five parts, a simple yet effect

SUBMITTER: Yuan L 

PROVIDER: S-EPMC10383990 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature