In video surveillance, classification 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 CO-variances (ARCO), and a multi-class classification framework operating on 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 classification framework consists in instantiating a new multi-class boosting method, working on the manifold Sym(d)(+) of symmetric positive definite d x d (covariance) matrices. As practical applications, we consider different surveillance tasks, such as head pose classification and pedestrian detection, providing novel state-of-the-art performances on standard datasets.

Multi-class Classification on Riemannian Manifolds for Video Surveillance

Tosato, Diego;Farenzena, Michela;Murino, Vittorio;Cristani, Marco
2010-01-01

Abstract

In video surveillance, classification 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 CO-variances (ARCO), and a multi-class classification framework operating on 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 classification framework consists in instantiating a new multi-class boosting method, working on the manifold Sym(d)(+) of symmetric positive definite d x d (covariance) matrices. As practical applications, we consider different surveillance tasks, such as head pose classification and pedestrian detection, providing novel state-of-the-art performances on standard datasets.
2010
9783642155512
Pedestrian detection; Riemannian manifold; Feature Extraction
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/472381
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