Part-based human detection on Riemannian manifolds
In this paper we propose a novel part-based framework for pedestrian detection. We model a human as a hierarchy of fixed overlapped parts, each of which described by covariances of features. Each part is modeled by a boosted classifier, learnt using Logitboost on Riemannian manifolds. All the classifiers are then linked to form a high-level classifier, through weighted summation, whose weights are estimated during the learning. The final classifier is simple, light and robust. The experimental results show that we outperform the state-of-the-art human detection performances on the INRIA person dataset.