Multivariate Relevance Vector Machines for Tracking
Multivariate Relevance Vector Machines for Tracking
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DOI：
10.1007/11744078_10
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发表时间：
2006-05
期刊：
影响因子：
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通讯作者：
A. Thayananthan;R. Navaratnam;B. Stenger;P. Torr;R. Cipolla
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文献类型：
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作者：
A. Thayananthan;R. Navaratnam;B. Stenger;P. Torr;R. Cipolla
本文提出了一种基于学习的方法来跟踪关节人体运动从一个单一的摄像机。为了解决姿态模糊的问题，从图像特征到状态空间的一对多映射是使用一组相关向量机学习的，扩展到处理多变量输出。图像特征是通过将不同的形状模板与图像进行匹配而获得的Hausdorff匹配分数，其中多变量相关向量机（MVRVM）选择这些模板的稀疏集合。我们证明，这些Hausdorff功能减少了估计误差的形状上下文直方图相比，在混乱。该方法适用于从一个单一的输入帧的姿态估计问题，并嵌入在一个概率跟踪框架，包括时间信息。我们将该算法应用于3D手部跟踪和完整的人体跟踪。
This paper presents a learning based approach to tracking articulated human body motion from a single camera. In order to address the problem of pose ambiguity, a one-to-many mapping from image features to state space is learned using a set of relevance vector machines, extended to handle multivariate outputs. The image features are Hausdorff matching scores obtained by matching different shape templates to the image, where the multivariate relevance vector machines (MVRVM) select a sparse set of these templates. We demonstrate that these Hausdorff features reduce the estimation error in clutter compared to shape-context histograms. The method is applied to the pose estimation problem from a single input frame, and is embedded within a probabilistic tracking framework to include temporal information. We apply the algorithm to 3D hand tracking and full human body tracking.