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 COvariances (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×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.
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Tosato, D., Farenzena, M., Spera, M., Murino, V., Cristani, M. (2010). Multi-class Classification on Riemannian Manifolds for Video Surveillance. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15552-9_28
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DOI: https://doi.org/10.1007/978-3-642-15552-9_28
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