基于时空特征融合的交通警察手势识别OA北大核心CSTPCD

In recent years,with the development of human pose estimation technology,gesture recognition technology based on skeleton key points comes into being.This paper proposes a GCPM-AGRU model for gesture recognition of traffic police.In order to locate the key points of human body more accurately,the convolution pose machine(CPM)is improved.Firstly,the idea of residuals,channel split and channel shuffle are added to the feature extraction module,so that the designed feature extraction module can better extract image features.In addition,the parallel multi-branch Incep-tion4d structure is added in the first stage of CPM,which makes the CPM network have the idea of multi-scale feature fusion,and effectively improves the problem of human key point location.Secondly,a GRU based on attention mecha-nism is proposed,which allocates different weights to each frame to achieve different degrees of attention to each frame,so as to obtain better time information.Finally,it combines the spatio-temporal feature information to carry out traffic police gesture recognition.The accuracy of traffic police gesture recognition reaches 93.7%,which is 2.95 percentage points higher than before the improvement of network.