关键词: 人体姿态估计, 高分辨网络, 多尺度

Abstract: Human pose estimation has become increasingly important in many fields such as animation design, security monitoring, and motion analysis. However, current human pose estimation algorithms focus on accuracy, leading to complex networks with high computational costs, making it difficult to apply them on mobile devices and embedded platforms. To address this challenge, this paper proposes the DNSNet, a multi-scale human pose estimation network that combines dynamic split convolution and normalized attention. Firstly, the bottleneck layer DKASCneck of the high-resolution network is redesigned using dynamic split convolution and dynamic kernel aggregation operations. This avoids excessive use of large convolution kernels, reduces computational costs while enhancing the ability of the network to extract useful features. Secondly, the NAMPCblock, a basic module using partial convolution and normalization-based attention mechanism, is introduced. This module reduces computational redundancy and memory access while enhancing information interaction across channels and spatial dimensions. Finally, the output feature fusion method of the network is redesigned based on multi-resolution features and deconvolution to improve the accuracy of heatmap regression predictions. Experimental results show that compared to high-resolution networks, on the COCO validation set, the average accuracy of the proposed network model is increased by 2.1 percentage points, the computational complexity is reduced by 32.4% and the model parameters are reduced by 71.9%. On the MPII validation set, the computational complexity is reduced by 38.9%, and the model parameters are reduced by 71.9%. The experimental data demonstrate that the proposed network significantly reduces network complexity while slightly improving detection accuracy.

Key words: human pose estimation, high-resolution network, multi-scale