Human Pose Estimation Based on Attention Mechanism and High-resolution Network
Human pose estimation aims to accurately identify key point positions and postures from images or videos,which is essential for behavior recognition,human-computer interaction,etc.The high-resolution network can extract the key point features of the human body containing multi-scale information from the image,but it mainly focuses on the feature information within the local range of the image,and it is difficult to capture the long-distance dependence between joints,so it is susceptible to complex background,occlusion and other factors,which limit the accuracy.In order to solve the problems faced by high-resolution networks in human pose estimation,this paper proposes a deep learning module that integrates attention mechanism and high-resolution network called C2F-CBAM,which combines the advantages of C2F module and CBAM module,and significantly improves the accuracy of the model in identifying key points by combining advanced feature extraction technology and enhanced attention mechanism.In addition,the C2F-CBAM module is embedded in the key position of the HRNet network,so that the method can better integrate and synthesize feature information at different scales,which not only enhances the adaptability of the model to various human postures and image resolutions,but also effectively deals with complex backgrounds and occlusions.Experimental results show that the proposed model has significant advantages over other methods in the COCO2017 validation set,and the average accuracy is improved by 0.9 compared with the traditional HRNet network,which fully verifies the effectiveness and superiority of the model.
human posture estimationattention mechanismshigh-resolution networksC2F-CBAM modulecritical point detection
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