Abstract

In video surveillance, classification of visual data can be very hard, due to the scarce resolution and the noise characterizing the sen- sors’ 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 covari- ance matrices of image features, able to extract information from data at prohibitive low resolutions. The proposed classification framework con- sists in instantiating a new multi-class boosting method, working on the d of symmetric positive definite d × d (covariance) ma- manifold S ym+ trices. 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.