Multi-class Classification on Riemannian manifolds for Video Surveillance

Mauro Spera, Diego Tosato, Michela Farenzena, Marco Cristani, Vittorio Murino

Research output: Chapter in Book/Report/Conference proceedingChapter

49 Citations (Scopus)

Abstract

In video surveillance, classication of visual data can be very hard, due to the scarce resolution and the noise characterizing the sensors' data. In this paper, we propose a novel feature, the ARray of COvariances (ARCO), and a multi-class classifcation framework operatingon Riemannian manifolds. ARCO is composed by a structure of covariance matrices of image features, able to extract information from data at prohibitive low resolutions. The proposed classication framework consists in instantiating a new multi-class boosting method, working on the manifold Sym+ of symmetric positive defnite d x d (covariance) matrices. As practical applications, we consider different surveillance tasks, such as head pose classifcation and pedestrian detection, providing novel state-of-the-art performances on standard datasets.
Original languageEnglish
Title of host publicationComputer Vision - ECCV 2010
Pages378-391
Number of pages14
Volume6312
Publication statusPublished - 2010

Publication series

NameLECTURE NOTES IN COMPUTER SCIENCE

Keywords

  • Riemannian Manifolds, Video Surveillance, Multi-class Boosting, Head Pose Estimation, Pedestrian Detection

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