Abstract
Human posture estimation is a prominent research topic in the fields of human-com-puter interaction,motion recognition,and other intelligent applications.However,achieving high accuracy in key point localization,which is crucial for intelligent applications,contradicts the low detection accuracy of human posture detection models in practical scenarios.To address this issue,a human pose estimation network called AT-HRNet has been proposed,which combines convolu-tional self-attention and cross-dimensional feature transformation.AT-HRNet captures significant feature information from various regions in an adaptive manner,aggregating them through convolu-tional operations within the local receptive domain.The residual structures TripNeck and Trip-Block of the high-resolution network are designed to further refine the key point locations,where the attention weight is adjusted by a cross-dimensional interaction to obtain more features.To vali-date the effectiveness of this network,AT-HRNet was evaluated using the COCO2017 dataset.The results show that AT-HRNet outperforms HRNet by improving 3.2%in mAP,4.0%in AP75,and 3.9%in APM.This suggests that AT-HRNet can offer more beneficial solutions for human posture estimation.
基金项目
National Natural Science Foundation of China(61975015)
Research and Innovation Project for Graduate Students at Zhongyuan University of Technology(YKY2024ZK14)
NETL
NSTL
万方数据