关键词: 人体姿态估计, 坐标回归, 状态空间模型, 轻量化, 软标签

Abstract: In the field of human pose estimation, heatmap-based methods suffer from the problems of quantization error, high computational complexity, and the need to post-process the heatmap as well. To address the above problems, a lightweight human pose estimation model based on fused feature state space model was proposed, namely Lite-SimCC, using the SimCC method of coordinate regression as a baseline. ShuffleNetV2 was adopted as the backbone network to replace the original High-Resolution Net (HRNet), which simplified to a single-branch form and realizes the model lightweighting. To mitigate the loss of accuracy, a large kernel convolution was introduced to extract global feature information. A state space model with fused features was further designed to handle both local and full long sequence features to enhance the characterization of key points. Finally, a soft-label based loss function was proposed to replace the traditional one-hot loss calculation. Experimental results show that compared with the baseline method SimCC, Lite-SimCC has the parameters decreased by 87.1%, and the Average Precision (AP) is improved by 1% to 71.8% on the COCO2017 test set. And the experiments on the MPII dataset also prove that Lite-SimCC also effectively reduces the number of parameters of the model based on the guaranteed detection accuracy.

Key words: human pose estimation, coordinate regression, state space model, lightweight, soft-label