传感技术学报2025，Vol.38Issue(3)：504-510,7.DOI:10.3969/j.issn.1004-1699.2025.03.017
基于压缩视频的驾驶行为识别方法
Driving Action Recognition Method Based on Compressed Videos
摘要
Abstract
Targeting at the problem of not being able to achieve high accuracy while having fast recognition speed,a multi-branch light-weight driving action recognition framework based on compressed video is proposed.First,the compressed video is direct processed into three branches,and in order to reduce the computational cost,a lightweight convolutional neural network is used for spatio-temporal modeling.One branch captures appearance cues by using a lightweight 2D convolutional network,and the other two branches employ a lightweight 3D convolutional network to learn temporal information from motion vectors and residual frames,respectively.The test results of the three branches are combined to obtain the final accuracy.In addition,a teacher model is introduced to guide the lightweight mod-el,and complementary knowledge is extracted from the high-capacity spatio-temporal deep learning model,which is migrated to the pro-posed multi-branch lightweight model to further improve the recognition accuracy.The experimental results show that the proposed framework has good feasibility and effectiveness for driving action recognition.关键词
行为识别/压缩视频/轻量化网络/时空建模/知识蒸馏Key words
action recognition/compressed videos/lightweight networks/spatio-temporal modeling/knowledge distillation分类
计算机与自动化引用本文复制引用
帅真,杨会成,胡耀聪,林园园,李雯婷..基于压缩视频的驾驶行为识别方法[J].传感技术学报,2025,38(3):504-510,7.基金项目
国家自然科学基金资助项目(62203012) （62203012）
安徽省重点实验室开放课题项目(JCKJ2022A07) （JCKJ2022A07）
