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.
In REF , Tuzel et al. represented the space of d-dimensional nonsingular covariance matrices extracted from training human patches, as connected Riemannian manifold.
Human Detection via Classification on Riemannian Manifolds
2007 IEEE Conference on Computer Vision and Pattern Recognition
2007 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science