Visual Object Tracking with Online Learning on Riemannian Manifolds by One-Class Support Vector Machines
Paper i proceeding, 2014

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.

Riemannian manifold

support vector machines

online learning

one-class classification

covariance matrix

Visual object tracking

Författare

Yixiao Yun

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Keren Fu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Jie Yang

Shanghai Jiao Tong University

IEEE International Conference on Image Processing (ICIP 2014), Oct.27 - 30, 2014, Paris, France

1902-1906

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Geometri

Systemvetenskap

Signalbehandling

Datorseende och robotik (autonoma system)

DOI

10.1109/ICIP.2014.7025381

ISBN

978-147995751-4

Mer information

Senast uppdaterat

2018-03-07