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Visual Object Tracking with Online Learning on Riemannian Manifolds by One-Class Support Vector Machines

Yixiao Yun (Institutionen för signaler och system, Signalbehandling) ; Keren Fu (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling) ; Jie Yang
IEEE International Conference on Image Processing (ICIP 2014), Oct.27 - 30, 2014, Paris, France p. 1902-1906. (2014)
[Konferensbidrag, refereegranskat]

This paper addresses issues in video object tracking. We propose a novel method where tracking is regarded as a one-class classification problem of domain-shift objects. The proposed tracker is inspired by the fact that the positive samples can be bounded by a closed hypersphere generated by one-class support vector machines (SVM), leading to a solution for robust learning of target model online. The main novelties of the paper include: (a) represent the target model by a set of positive samples as a cluster of points on Riemannian manifolds; (b) perform online learning of target model as a dynamic cluster of points flowing on the manifold, in an alternate manner with tracking; (c) formulate geodesic-based kernel function for one-class SVM on Riemannian manifolds under the log-Euclidean metric. Experiments are conducted on several videos, results have provided support to the proposed method.

Nyckelord: Visual object tracking, support vector machines, one-class classification, online learning, Riemannian manifold, covariance matrix

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Denna post skapades 2014-07-24. Senast ändrad 2016-01-05.
CPL Pubid: 200719


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