  For covariance-based image descriptors, taking into account the curvature of
the corresponding feature space has been shown to improve discrimination
performance. This is often done through representing the descriptors as points
on Riemannian manifolds, with the discrimination accomplished on a tangent
space. However, such treatment is restrictive as distances between arbitrary
points on the tangent space do not represent true geodesic distances, and hence
do not represent the manifold structure accurately. In this paper we propose a
general discriminative model based on the combination of several tangent
spaces, in order to preserve more details of the structure. The model can be
used as a weak learner in a boosting-based pedestrian detection framework.
Experiments on the challenging INRIA and DaimlerChrysler datasets show that the
proposed model leads to considerably higher performance than methods based on
histograms of oriented gradients as well as previous Riemannian-based
techniques.
