计算机科学
预处理器
邻接矩阵
卷积(计算机科学)
图形
人工智能
比例(比率)
模式识别(心理学)
邻接表
残余物
算法
理论计算机科学
人工神经网络
物理
量子力学
作者
Taoying Li,Xutong Li,Bo Ren,Ge Guo
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:73 (1): 295-309
标识
DOI:10.1109/tvt.2023.3308566
摘要
One essential issue in skeleton-based driver action recognition is that incomplete skeletons collected from real scenes would degrade model performance. However, existing models often ignore the missing joint preprocessing and tend to be over-parameterized. In this work, we propose (1) a padding strategy SmoothNode and (2) a skeleton-based Multi-Scale Excitation Graph Convolution Network (MSE-GCN). Firstly, SmoothNode, as a part of preprocessing, fills both missing frames and nodes in a smooth style and repairs the incomplete skeletons to a relatively complete state. Secondly, inspired by the efficient modeling ability of EfficientGCN in dynamic skeletons, the MSE-GCN model is designed to reason multi-scale spatial-temporal features through two improvements, i.e., Spatial Graph Convolution layer based on the Independent Self-connecting formulation mode (SGC-IS) and Multi-Scale Wrapper Fused Spatial-Temporal Excitation layer (MSW-FSTE). SGC-IS optimizes the normalized adjacency matrix formulation mode and strengthens connections between each pair of nodes, while MSW-FSTE excites temporal patterns with the global spatiotemporal features in a hierarchical residual-like style and learns multi-scale features in the temporal domain. By coupling these proposals, we develop SMOMS, a driver behavior recognition framework. Extensive experiments on three released datasets, i.e., Drive&Act, 3MDAD, and EBDD, demonstrate that the proposed SMOMS framework outperforms other methods.
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