Issue No. 10 - October (2008 vol. 30)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.75
Oncel Tuzel , Rutgers University, Piscataway
Fatih Porikli , Mitsubishi Electric Research Labs., Cambridge
Peter Meer , Rutgers University, Piscataway
We present a new algorithm to detect pedestrian 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 INRIA and DaimlerChrysler pedestrian datasets where superior detection rates are observed over the previous approaches.
Object recognition, Scene Analysis, Image Processing and Computer Vision, Computing Methodologies, Vision and Scene Understanding, Machine learning
O. Tuzel, F. Porikli and P. Meer, "Pedestrian Detection via Classification on Riemannian Manifolds," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 1713-1727, 2008.