Impact of Dynamics on Subspace Embedding and Tracking of Sequences
Impact of Dynamics on Subspace Embedding and Tracking of Sequences
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DOI：
10.1109/cvpr.2006.148
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发表时间：
2006-06
期刊：
影响因子：
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通讯作者：
Kooksang Moon;V. Pavlovic
中科院分区：
文献类型：
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作者：
Kooksang Moon;V. Pavlovic
在本文中，我们研究的作用，动力学降维问题适用于序列。我们提出了一个新的家庭的边际自回归（MAR）模型，描述了所有稳定的自回归序列的空间，而不管他们的具体动态。我们应用MAR类模型作为概率序列子空间嵌入问题的序列先验。特别是，我们认为高斯过程的潜变量方法降维，并表明使用MAR先验可能会导致更好的估计序列子空间比传统的非顺序先验。然后，我们提出了一种学习方法估计非线性动态系统（NDS）模型，利用新的MAR先验。所提出的方法的实用性证明了几个合成数据集，以及在单目图像序列中跟踪3D关节数字的任务。
In this paper we study the role of dynamics in dimensionality reduction problems applied to sequences. We propose a new family of marginal auto-regressive (MAR) models that describe the space of all stable auto-regressive sequences, regardless of their specific dynamics. We apply the MAR class of models as sequence priors in probabilistic sequence subspace embedding problems. In particular, we consider a Gaussian process latent variable approach to dimensionality reduction and show that the use of MAR priors may lead to better estimates of sequence subspaces than the ones obtained by traditional non-sequential priors. We then propose a learning method for estimating nonlinear dynamic system (NDS) models that utilizes the new MAR priors. The utility of the proposed methods is demonstrated on several synthetic datasets as well as on the task of tracking 3D articulated figures in monocular image sequences.