3D People Tracking with Gaussian Process Dynamical Models
3D People Tracking with Gaussian Process Dynamical Models
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DOI：
10.1109/cvpr.2006.15
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发表时间：
2006-06
期刊：
影响因子：
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通讯作者：
R. Urtasun;David J. Fleet;P. Fua
中科院分区：
文献类型：
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作者：
R. Urtasun;David J. Fleet;P. Fua
我们提倡使用高斯过程动态模型（GPDM）来学习人体姿态和运动先验，以进行3D人体跟踪。GPDM提供了人体运动数据的低维嵌入，其密度函数为接近训练数据的姿势和运动提供了更高的概率。使用贝叶斯模型平均，可以从相对少量的数据中学习GPDM，并且它可以优雅地推广到训练集之外的运动。在这里，我们修改的GPDM允许学习的运动与显着的风格变化。由此产生的先验是有效的跟踪一系列的人类行走风格，尽管弱和嘈杂的图像测量和显着的闭塞。
We advocate the use of Gaussian Process Dynamical Models (GPDMs) for learning human pose and motion priors for 3D people tracking. A GPDM provides a lowdimensional embedding of human motion data, with a density function that gives higher probability to poses and motions close to the training data. With Bayesian model averaging a GPDM can be learned from relatively small amounts of data, and it generalizes gracefully to motions outside the training set. Here we modify the GPDM to permit learning from motions with significant stylistic variation. The resulting priors are effective for tracking a range of human walking styles, despite weak and noisy image measurements and significant occlusions.