关键词: 行为识别, 关节点, 时空图卷积网络（ST-GCN）, 分区策略, 学习率

Abstract:

Bone-based action recognition is widely concerned because it is not affected by the physical characteristics of the human body, simply and clearly conveys important information about human behavior recognition. Traditional application skeleton modeling usually relies on the artificial setting of traversal rules, which results in limited expressive power and less influential. Therefore, based on the recent popular Spatio-Temporal Graph Convolutional Network（ST-GCN） model, a new partitioning strategy for dividing skeleton joint points is proposed. Compared with the original partition method, this strategy strengthens the relationship between the relative positions of the body, which is conducive to improving the temporal and spatial correlation of skeleton joint point information. At the same time, during the training process, different iterative learning rates are set to further improve the recognition accuracy. The recognition effect is compared with the existing methods on two large-scale data sets Kinetics and NTU-RGB+D, the experimental results show the effectiveness of the proposed method.

Key words: behavior recognition, joint points, Spatio-Temporal Graph Convolutional Network（ST-GCN）, partition strategy, learning rate