| Human Detection via Classification of Riemannian Manifolds |
| Citation: |
* Tuzel, O.; Porikli, F.; Meer, P., "Human Detection via Classification on Riemannian Manifolds", IEEE Computer Society Conference on Computer Vision & Pattern Recognition (CVPR), June 2007 (CVPR 2007) |
| Date: | July 2007 |
| MERL Contacts: | Fatih Porikli,
Fatih Porikli |
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. |
|
