Pedestrian Detection via Classification on Riemannian Manifolds
Top Cited Papers
- 31 March 2008
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 30 (10) , 1713-1727
- https://doi.org/10.1109/tpami.2008.75
Abstract
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.Keywords
This publication has 40 references indexed in Scilit:
- Eigenvalue distributions for some correlated complex sample covariance matricesJournal of Physics A: Mathematical and Theoretical, 2007
- Riemannian geometry for the statistical analysis of diffusion tensor dataSignal Processing, 2007
- Nonlinear Mean Shift for Clustering over Analytic ManifoldsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Rapid object detection using a boosted cascade of simple featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Simultaneous multiple 3D motion estimation via mode finding on Lie groupsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Sharing features: efficient boosting procedures for multiclass object detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Detecting pedestrians using patterns of motion and appearancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)The Annals of Statistics, 2000
- Example-based learning for view-based human face detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998
- Low-dimensional procedure for the characterization of human facesJournal of the Optical Society of America A, 1987