Human Detection via Classification on Riemannian Manifolds
Human Detection via Classification on Riemannian Manifolds
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DOI：
10.1109/cvpr.2007.383197
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发表时间：
2007-06
期刊：
影响因子：
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通讯作者：
Oncel Tuzel;F. Porikli;P. Meer
中科院分区：
文献类型：
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作者：
Oncel Tuzel;F. Porikli;P. Meer
我们提出了一种新的算法，利用协方差矩阵作为对象描述符检测静止图像中的人。由于这些描述符不位于向量空间上，因此众所周知的机器学习技术不足以学习分类器。d维非奇异协方差矩阵的空间可以表示为连通的黎曼流形。我们提出了一种新的方法，通过结合有关空间几何的先验信息来对黎曼流形上的点进行分类。该算法在INRIA人类数据库上进行测试，观察到上级检测率超过以前的方法。
We present a new algorithm to detect humans in still images utilizing covariance matrices as object descriptors. Since these descriptors do not lie on a vector space, well known machine learning techniques are not adequate to learn the classifiers. The space of d-dimensional nonsingular covariance matrices can be represented as a connected Riemannian manifold. We present a novel approach for classifying points lying on a Riemannian manifold by incorporating the a priori information about the geometry of the space. The algorithm is tested on INRIA human database where superior detection rates are observed over the previous approaches.