Head pose estimation method based on improved HopeNet
Aiming at the poor accuracy of the head pose estimation algorithm based on no prior knowledge in complex background images and multi-scale image scenes,a head pose estimation method based on improved HopeNet is proposed.Firstly,the feature fusion structure is added to the backbone network structure to make the model make full use of the deep and shallow feature information of the network and improve the feature analysis power of the model.Then feature squeeze and excitation module is added to the residual structure of the backbone network,so that the network can adaptively learn the weight information of different feature layers and the model can pay more attention to the target information.Experimental results show that compared with HopeNet,the accuracy of the pro-posed method on AFLW2000 dataset is improved by 31.15％,and the average error is reduced to 4.20 °.Mean-while,the proposed method has good robustness in complex background image scenes.
head pose estimationHopeNetcharacteristics of the fusioncharacteristic compression and ex-citationadaptive learning
万方数据
维普
