Pedestrian detection via classification on Riemannian manifolds
We present a new algorithm to detect pedestrians in still images utilizing covariance matrices as object descriptors. Since the descriptors do not form a vector space, well-known machine learning techniques are not well suited to learn the classifiers. The space of d-dimensional nonsingular covariance matrices can be represented as a connected Riemannian manifold. The main contribution of the paper is a novel approach for classifying points lying on a connected Riemannian manifold using the geometry of the space. The algorithm is tested on the INRIA and DaimlerChrysler pedestrian data sets where superior detection rates are observed over the previous approaches.
pedestrian detection classification riemannian manifolds object descriptors symmetric positive definite matrices boosting