A Re-evaluation of Pedestrian Detection on Riemannian Manifolds

Abstract--Boosting covariance data on Riemannian manifolds has proven to be a convenient strategy in a pedestrian detection context. In this paper we show that the detection performances of the state-of-the-art approach of Tuzel et al. [7] can be greatly improved, from both a computational and a qualitative point of view, by considering practical and theoretical issues, and allowing also the estimation of occlusions in a fine way. The resulting detection system reaches the best performance on the INRIA dataset, setting novel state-of-theart results. Keywords-Pedestrian Detection, Riemaniann Manifolds, Boosting In Computer Vision, detecting people in images is a crucial yet hard task; this is due to the presence of many acquisition settings and the large variations of human appearance and pose. Across the many nowadays techniques, whose recent samples are [6], [7], [8], the ensemble-offeatures based methods [3], [7] are very promising. They are based on boosting [4], which is reckoned t...

• Type: Journal
• Year: 2010
• Where: ICPR
• Authors: Diego Tosato, Michela Farenzena, Marco Cristani, Vittorio Murino
• Keywords: Boosting, Computer Vision, Detection, ICPR 2010, Riemannian Manifold