论文类型：论文集
第一作者：李仕奇
通讯作者：项翔
发表刊物：IAPR International Conference on Pattern Recognition (ICPR) 2022 Workshops
收录刊物：EI
所属单位：Springer Lecture Notes in Computer Science
刊物所在地：德国
学科门类：工学
一级学科：计算机科学与技术
项目来源：华中科技大学自主创新研究基金项目(2021XXJS096)基于行为观测的疼痛自动评估的方法研究(项翔,2021-04至2024-03)
文献类型：C
关键字：Pose estimation, 3D pose, human pose, lightweight, attention
发表时间：2022-06-20
摘要：Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed. In this paper, we lighten the computation cost and parameters of the deconvolution head network in SimpleBaseline and introduce an attention mechanism that utilizes original, inter-level, and intra-level information to intensify the accuracy. Additionally, we propose a novel loss function called heatmap weighting loss, which generates weights for each pixel on the heatmap that makes the model more focused on keypoints. Experiments demonstrate our method achieves a balance between performance, resource volume, and inference speed. Specifically, our method can achieve 65.3 AP score on COCO test-dev, while the inference speed is 55 FPS and 18 FPS on the mobile GPU and CPU, respectively.