吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (1): 251-258.doi: 10.13229/j.cnki.jdxbgxb.20220280
• 通信与控制工程 • 上一篇
基于弱监督迁移网络的3D人体关节点识别
孙志勇1(
),李宏友2,叶俊勇1()
- 1.重庆大学 光电技术教育部重点实验室，重庆 400044
2.重庆警察学院 基础教研部，重庆 401331
3D human joint point recognition based on weakly supervised migration network
Zhi-yong SUN1(),Hong-you LI2,Jun-yong YE1()
- 1.Key Laboratory of Optoelectronic Technology of the Ministry of Education，Chongqing University，Chongqing 400044，China
2.Department of Basic Education，Chongqing Police College，Chongqing 401331，China
摘要：
针对2D图像缺少深度信息，行为姿态空间结构信息不完备的问题，提出一种基于弱监督迁移网络的3D人体关节点识别方法。首先，提出一种用于真实图像的端到端3D人体姿态估计框架，使用2D与3D混合标签图像对深度神经网络进行训练，在2D人体姿态识别子网络中，添加深度回归模块对2D人体姿态识别子网络进行改进，解决3D人体姿态识别出现的深度歧义性问题；其次，在3D人体姿态识别子网络中，引入3D几何约束对人体姿态识别进行规范化操作，针对无真实深度标签的情况，可更好地学习深度特征，有效解决存在遮挡情况的人体姿态识别问题。在Human 3.6M和MPII数据集中关节点预测平均误差低于其他方法，具有更好的3D人体姿态识别效果。
中图分类号:
- TP391
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