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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(More)
In surveillance applications, head and body orientation of people is of primary importance for assessing many behavioral traits. Unfortunately, in this context people are often encoded by a few, noisy pixels so that their characterization is difficult. We face this issue, proposing a computational framework which is based on an expressive descriptor, the(More)
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