关键词: 图卷积网络, 数据增强, 行为识别, 姿势估计, 电梯乘客

Abstract: The challenges of low accuracy and poor generalization performance in recognizing abnormal behaviors of elevator passengers were attributed to the lack of sufficiently diverse abnormal behavior data. To address this issue, a Dynamic Graph Convolutional Network-based Behavior data Augmentation (DGCN-BA) framework was proposed. Firstly, a Dynamic Graph Convolutional Network (DGCN) was constructed to capture the spatial relationships and motion correlations among different human joints in the behaviors of elevator passengers. These features were utilized to enhance pose data, generating richer and more realistic pose sequences. Secondly, the pose sequences were used to construct human actions in a virtual elevator scene, generating a large amount of abnormal behavior video data for elevator passengers. Finally, to validate the effectiveness of DGCN-BA, experiments were conducted on public datasets Human3.6M, 3DHP, and MuPoTS-3D, as well as on a self-constructed dataset. Experimental results show that, compared to other data augmentation methods (JMDA (Joint Mixing Data Augmentation) and DDPMs (Denoising Diffusion Probabilistic Models)), DGCN-BA reduces the average MPJPE on the Human3.6M dataset by 2.9mm and 1.5mm, respectively. It more effectively handles pose estimation tasks, generates diverse and realistic abnormal behavior data, and significantly improves the recognition of elevator passenger abnormal behaviors in video-based scenarios.

Key words: graph convolutional network；data augmentation；behavior recognition；pose estimation；elevator passenger
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