基于空间约束的人体运动最大似然估计

2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013) Pub Date : 2013-11-14 DOI:10.1109/ICSPCC.2013.6663910

Wanyi Li, Jifeng Sun, Xin Zhang, Yuan-Chun Wu

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引用次数: 5

摘要

提出了一种新的基于空间约束的最大似然估计(SC-based MLE)方法来处理不完整人体运动周期的潜在变量数据，改进了GPDM的学习和估计，使GPDM能够学习不完整人体运动周期的样本来估计新的人体运动。该方法减少了GPDM学习对人体完整运动周期训练样本的依赖，并节省了训练样本。通过分别使用不完整和完整的人体运动周期样本进行训练的人体运动估计实验，验证了所提方法的有效性和有效性。

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Spatial constraints-based maximum likelihood estimation for human motions

A new method of spatial constraints-based maximum likelihood estimation (SC-based MLE) is proposed to process latent variables data of incomplete human motions cycle, and improve the GPDM learning and estimation in this paper, which can make the GPDM learn the samples of incomplete human motions cycle to estimate the new human motions. The proposed method has the GPDM learning less depend on training samples of the complete human motions cycle, and save the training samples. We verify the validity and efficiency of the proposed method, through the experiments of human motions estimation using the samples of incomplete and complete human motions cycle for training respectively.

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来源期刊

2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013)

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