重庆理工大学学报2025，Vol.39Issue(19)：1-12,12.DOI:10.3969/j.issn.1674-8425(z).2025.10.001
融合注意力机制的驾驶人行为识别模型研究
Research on driver behavior recognition model with fusion attention mechanism
摘要
Abstract
Taking driver behavior recognition in assisted driving as the research object,this paper designs a functional module based on the fusion of time and channel attention mechanism.A driver behavior recognition model is built to improve recognition accuracy.First,a self-built multi-perspective driver behavior data-set is built with 4 shooting perspectives,10 driving behaviors,and 1 148 video data.Then,a TCAM-R(2+1)D driver behavior recognition model is built based on the(2+1)D convolution module.With the ResNET backbone network,a functional module integrating time and channel attention mechanisms is proposed,enhancing the model's ability to extract temporal information.Finally,the Adabond optimizer is employed to train the model and improve its recognition accuracy and generalization ability.Experimental results show by increasing the attention mechanism of the model,the self-built data-set improves the accuracy of driver behavior recognition by 3.03%compared to the R(2+1)D model.A large-scale human motion data-set(HMBD51)is employed for ablation experiments.The fusion attention mechanism functional module's accuracy rises to 59.60%(an improvement of 1.93%),demonstrating its superior performances in fusion time and channel attention mechanism.关键词
深度学习/行为识别/注意力机制/R(2+1)D模型Key words
deep learning/behavior recognition/attention mechanism/R(2+1)D model分类
计算机与自动化引用本文复制引用
徐慧智,张原铭..融合注意力机制的驾驶人行为识别模型研究[J].重庆理工大学学报,2025,39(19):1-12,12.基金项目
国家自然科学基金项目(62371170) （62371170）
