Human Detection via Classification on Riemannian Manifolds

Oncel Tuzel, F. Porikli, P. Meer
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引用次数: 543

Abstract

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.
黎曼流形的分类人体检测
我们提出了一种利用协方差矩阵作为目标描述符来检测静止图像中的人的新算法。由于这些描述符不在向量空间上,因此已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黎曼流形上点的分类方法,该方法结合了空间几何的先验信息。该算法在INRIA人类数据库上进行了测试,检测率优于以往的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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