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Incremental learning is a hot topic in real time visual tracking. Covariance matrix descriptor was proposed recently. Here we proposed a incremental learning framework on this descriptor, by introducing the Log-Euclidean framework on tensors.

In the framework, the covariance matrices of image features in the five modes are used to represent object appearance. The Log-Euclidean Riemannian metric is used for statistics on the covariance matrices of image features. Further, we present an effective online Log-Euclidean Riemannian subspace learning algorithm which models the appearance changes of an object by incrementally learning a low-order Log-Euclidean eigenspace representation through adaptively updating the sample mean and eigenbasis. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over the time. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed framework.

Publications on this topic include:

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