Lightweight Cross-fusion Network on Human Pose Estimation for Edge Device

Xian Zhu, Xiaoqin Zeng, Wei Ma
2021 IEEE Access  
The deployment of human pose estimation on edge devices are essential task in computer vision. Due to memory and storage space limitations, it is difficult for edge devices to maintain implementing Convolutional Neural Networks, which deployed large-scale terminal platforms with abundant computing resources. This paper proposed novel Lightweight Cross-fusion Network on Human Pose Estimation with information sharing. Using state-of-the-art efficient neural architecture, and Ghost Net, as the
more » ... bone, which are gradually applying a cross-information fusion network for key points extraction in the baseline and strengthen phases. As a result, the computational cost significantly reduces, while maintaining feature confidence more accurate and predicting key points heatmaps more precisely. Our network model can entirely execute on edge devices, and extensive self-comparison experiments have evaluated the architecture's effectiveness. The MS COCO 2017 dataset proved that the cross-fusion network is superior than other lightweight structures for pose estimation
doi:10.1109/access.2021.3065574 fatcat:r2zl54crdnb5dgtzqdm6owoxzi